Due to the rapid development of new generation information technology worldwide, universitiesâ cultivation of digital and intelligent talents has become a necessity of the times. To address the common challenges in traditional practice-oriented teaching, including outdated course design, insufficient authenticity of experimental environments, and cross-institutional trust deficits in student evaluation, this study takes the âDigital-Intelligence Workshopâ as the implementation context. Guided by the principles of emerging engineering education, a student-centered philosophy, and an OBE- and project-driven teaching framework, the study systematically improves the curriculum system. By introducing a blockchain-enabled data governance layer, this platform ensures the tamper-resistance of learning records, enables secure data sharing between universities and enterprises, and facilitates transparent smart-contract-based assessments. This article shows that by carrying out interdisciplinary collaboration and real enterprise case projects on the trusted training platform, studentsâ digital thinking and algorithm practice abilities have significantly improved, and teachers have also made progress in achievement transformation. Relevant teaching practices indicate that the blockchain-enabled âDigital Intelligence Workspaceâ effectively promotes the improvement of talent cultivation quality in universities, providing a feasible new approach and practical reference for undergraduate education reform in the era of intelligence.
Velian Prapatoni, Rizky Parlika, Firza Prima Aditiawan
Cryptocurrency markets are characterized by high volatility, rapid price fluctuations, and substantial uncertainty, creating challenges for investment risk interpretation. This study develops a descriptive risk-interpretation framework, rather than a price-prediction or decision-optimization model, by integrating multi-scale volatility analysis with technical indicators. A quantitative descriptive design was applied to approximately one year of historical hourly price data for Bitcoin and Ethereum, covering open, high, low, close, volume, and percentage change attributes. The data were chronologically sorted, numerically cleaned and normalized, transformed into log returns, and analyzed through rolling standard deviation. Volatility was estimated across three explicitly defined horizons: short-term 7-period, medium-term 30-period, and long-term 90-period rolling windows. Moving Average (MA), Relative Strength Index (RSI), and Average True Range (ATR) were then incorporated to contextualize trend direction, momentum, and fluctuation intensity. The results show that volatility is strongly horizon-dependent: short-term movements responded more sharply to market shocks, whereas longer horizons produced smoother risk patterns. Across the analyzed Bitcoin and Ethereum hourly series, the reported 42.3% short-term and 21.7% medium-term increases were calculated as relative differences against long-term rolling volatility during identified high-uncertainty intervals, not as predictive accuracy measures. These findings indicate that combining rolling volatility with MA, RSI, and ATR can improve the transparency of descriptive cryptocurrency risk assessment. The framework may support preliminary interpretation for novice or risk-averse investors, although it does not empirically test investor comprehension or subsequent decision quality.
Mohammad Quthbul Widad, Rizky Parlika, Firza Prima Aditiawan
Although Bitcoin is acknowledged as the largest cryptocurrency by market capitalization and trading volume in the world's financial market, investors face a great deal of risk and uncertainty due to its exceptionally high volatility and non-linear price changes. To provide a data-driven foundation for risk reduction and forecasting support, accurate modeling techniques are crucial. This work attempts to provide a thorough comparative analysis mapping the precise accuracyâefficiency trade-off between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models under a standardized Grid Search hyperparameter optimization pipeline using a recent Bitcoin closing-price dataset spanning from January 1, 2020, to January 1, 2026. The research methodology follows a structured data science pipeline, beginning with data acquisition from Yahoo Finance, followed by preprocessing using Min-Max Scaling fitted strictly on the training partition to eliminate data leakage. Model development involves an experimental approach where both LSTM and GRU neural controllers are tuned to extract optimal structural weights. The predictive precision of these models is rigorously evaluated using three standard metrics: MAE, RMSE, and MAPE, while processing throughput is measured via hardware execution times. The research findings indicate that the optimized LSTM model achieved superior one-step-ahead predictive precision with a MAPE of 2.32%, whereas the GRU model recorded a higher error rate of 3.94%. However, the GRU model demonstrated a significant advantage in computational efficiency, completing the training process 8.45 times faster than LSTM. In conclusion, while LSTM is recommended as a forecasting support tool for high-precision financial analysis, GRU remains a viable, parameter-efficient alternative for real-time monitoring on resource-constrained systems before real-world financial deployment.
Muhammad Diaz Syahmi Oktavian, Rizky Parlika, Firza Prima Aditiawan
The extreme price volatility of Bitcoin frequently prevents its widespread adoption. The persistent "Digital Gold" narrative often dominates its price analysis, largely ignoring the predictive value of strategic industrial commodities like Platinum Group Metals. This study aims to investigate whether integrating industrial metals specifically platinum and rhodium enhances the short-term forecasting accuracy of Bitcoin prices. Utilizing high-frequency 5-minute interval data over 729 days, this research applies a comparative quantitative approach using univariate and multivariate Long Short-Term Memory (LSTM) deep learning architectures. Results demonstrate the multivariate LSTM model achieves highly accurate forecasting, recording a Mean Absolute Percentage Error (MAPE) of 3.95% and a Root Mean Squared Error (RMSE) of 0.0598. Compared to the univariate baseline model (MAPE of 5.14%, RMSE of 0.0725), the multivariate approach demonstrates a notable decrease in error rates. This improvement suggests platinum and rhodium price movements contain useful informational value for Bitcoin forecasting, rather than mere random noise. Specifically, rhodium demonstrates strong predictive relevance for Bitcoin market movements. In conclusion, while not strictly proving causal structural integration, these findings highlight Bitcoin's sensitivity to the global real-sector economic cycle. Practically, these findings suggest investors can refine short-horizon forecasting and mitigate risk by monitoring industrial commodity prices. Given persistent nominal offset deviations, future research should prioritize explicit connectedness testing (e.g., lead-lag analysis) and develop a hybrid model incorporating Natural Language Processing (NLP) for news sentiment analysis.
Abstract This paper presents a provenance-preserving deep-learning framework for computational design screening and full-wave validation of high-isolation millimeter-wave cylindrical dielectric resonator antennas in a two-port MIMO configuration. The framework integrates Ansys HFSS, Python-based sampling, deep-learning prediction, and blockchain- and IPFS-based provenance within a reproducible computational workflow. An HFSS-Python pipeline generates 300 parametric CDRA geometries, each represented through binary image encoding and associated electromagnetic response data, including the reflection coefficient ( $$S_{11}$$ ) and the HFSS-exported total radiated electric-field response ( $$r_E$$ ). CNN, LSTM, CNNâLSTM, and multimodal ImageâCNNâLSTM models are trained to predict radiation behaviour and rank candidate designs through pseudo-ECC-based screening. The selected CDRA is subsequently assessed through full-wave HFSS simulation in an orthogonal MIMO arrangement. At 27.96 GHz, the validated design achieves an $$S_{11}$$ of $$-20.79$$ dB, an $$S_{21}$$ of $$-50.13$$ dB, an ECC of $$7.78\times 10^{-8}$$ , diversity gain close to 10 dB, TARC of $$-21.09$$ dB, and CCL of 0.02185 bits s $$^{-1}$$ Hz $$^{-1}$$ . The orthogonal layout also improves isolation by about 20.31 dB compared with the parallel arrangement. The generated simulation and learning artefacts are further registered through a local Ethereum development network integrated with IPFS to support authenticity, traceability, and tamper-evident record keeping. The study demonstrates a practical computational route for automated and verifiable antenna-design screening, linking data-driven optimization with provenance-preserved management of electromagnetic design artefacts.
Novan Ilham Ramadhan, Rizky Parlika, Ardhon Rakhmadi
Electronic voting (e-voting) systems continue to face challenges related to transparency, result validation, and duplicate voting prevention. Blockchain technology offers characteristics such as decentralization, transparency, and immutability that can support more auditable voting processes. This study presents a prototype implementation of a blockchain-based e-voting system using Ethereum smart contracts to support transparent vote recording, public auditability, and wallet-level double voting prevention. The system was implemented using Solidity-based smart contracts deployed on the Ethereum Sepolia Testnet and integrated with MetaMask for transaction authentication. Smart contracts manage election creation, candidate registration, voter registration, vote recording, duplicate vote prevention, and voting result finalization. An audit trail mechanism was implemented to allow voting activities and transaction records to be publicly verified through the Etherscan blockchain explorer. All predefined functional testing scenarios were executed successfully. The evaluation results indicate an average gas usage of 75,109 gas, an estimated transaction cost of 0.000113 ETH, and a transaction latency of approximately 4â5 seconds. The implemented wallet validation mechanism and hasVoted mapping effectively prevent duplicate voting attempts associated with the same wallet address. However, the proposed system represents a prototype-level evaluation conducted on the Ethereum Sepolia Testnet and does not provide voter identity verification, ballot anonymity, or real-world election readiness. The findings demonstrate the feasibility of Ethereum smart contracts for transparent auditability and wallet-level double voting prevention in blockchain-based voting environments.
Tapasi Bhattacharjee, Amalendu Singha Mahapatra, Dipika Pramanik
Educational crowdfunding has emerged as a promising approach to provide educational resources to underprivileged communities. Conventional systems often suffer from a lack of transparency, weak accountability, inefficient allocation of funds, and inadequate traceability of resource use. To address these issues, the present study proposes an intelligent and efficient educational supply chain management system, âEduDonateBlock.â It uses a blockchain-based crowdfunding framework to ensure transparency, accountability, and efficiency. Decentralization, immutability, and verifiable transactions are supported in educational campaigns. The entire workflow is decomposed into modular smart contracts. These are the identity and access contract (IAC), campaign and donation contract (CDC), verification and allocation contract (VAC), and supply chain and tracking contract (SCTC). These contracts are designed to ensure traceability, accountability, and efficient resource allocation among donors, educational institutions, and administrators. The mathematical framework of EduDonateBlock determines the optimal level of blockchain transparency. This minimizes the Total Expected Cost (TEC) of smart-contract operations. Numerical analysis identifies an optimal transparency level of 87.16% on-chain integration. This finding underscores the economic trade-off between transaction costs and the benefits of automation, operational efficiency, and reduced fraud risk. The proposed framework achieves a campaign success probability of 89.45% and an institutional payoff of Rs. 11,335.99. Furthermore, executing smart contracts requires 0.0044 ETH, and the average latency remains at 6.25 s. The simulation results show that EduDonateBlock offers a more efficient, reliable, and transparent solution for decentralized educational crowdfunding and socially impactful digital supply chains.
ViTs have achieved excellent performance in image recognition tasks, but their large parameter counts and high computational complexity limit their deployment on resource-constrained devices. Most existing ViT pruning methods adopt class-agnostic pruning strategies, which fail to distinguish the diverse structural requirements of different target classes. As a result, they are prone to removing critical features, leading to class-wise accuracy imbalance in practical deployment. To address this issue, this paper proposes a class-aware joint pruning framework for ViTs, which collaboratively compresses the model from two orthogonal dimensions: MLP neurons and visual tokens. Specifically, (1) based on first-order Taylor expansion, we quantify the contribution of each MLP neuron to the target classes and adaptively prune redundant neurons to achieve structured compression, followed by lightweight fine-tuning on the target class subset; (2) we propose a Class-Guided Token Selection (CGTS) method, which constructs class prototype vectors using a few support samples of the target classes and then dynamically selects patch tokens that are semantically highly relevant to the target classes during inference in a zero-shot manner, requiring no additional training or fine-tuning. The two modules complement each other, achieving dual compression from the parameter dimension and the inference data dimension. Experiments on CIFAR-100 and TinyImageNet datasets using DeiT-Tiny/Small models demonstrate that, compared with state-of-the-art pruning methods, our method reduces GMACs on target class subsets by up to 48%, improves inference speed by nearly 50%, and requires only 0.8 KB of additional storage overhead per subset, ultimately achieving a superior trade-off among accuracy, computational efficiency, and storage overhead.
TT-G41: A Hybrid Post-Quantum Cryptosystem with Ly-Algebraic Quasi-Equivalence Index and Symmetry-Modulated Padding Chloe J. Tully Independent Researcher https://doi.org/10.5281/zenodo.21860133 Orcid: https://orcid.org/0009-0007-5661-7332 Version: 1.1 August 2026 ======================================== Abstract TT-G41 is a hybrid post-quantum cryptosystem that unifies a five-dimensional Ly-Algebraic Quasi-Equivalence Index (QEI) with an NTRU-style lattice layer. A novel symmetry-modulated padding mechanism injects structured noise scaled by s = exp(-alpha Ă QEI), establishing a direct causal link between the geometric coherence of the input and the entropy of the ciphertext. Empirical evaluation over 4000 trials yields a logistic security bound P(fail) = (1 + exp[15.57(QEI - 0.209)])^(-1) with R-squared = 0.980. A deterministic hard gate at QEI = 0.12 converts geometric incoherence into an immediate, deterministic decryption rejection, providing an active anti-tamper primitive resilient to partial-message side channels. The construction demonstrates that Ly-Algebraic geometric coherence can serve as a measurable quantum-resilient agent for cryptographic failure probability, establishing a new class of symmetry-gated post-quantum protocols. Keywords: post-quantum cryptography, Lie algebra, Quasi-Equivalence Index, NTRU, symmetry-modulated padding, geometric security bound, anti-tamper encryption ======================================== 1. Introduction Most post-quantum constructions treat geometric or algebraic structures solely as a source of hardness assumptions. TT-G41 inverts this relationship: it elevates a continuous geometric measure, the Quasi-Equivalence Index (QEI) derived from a graded Lie algebra, into an active security control surface. The system combines three elements: 1. A five-dimensional graded algebra with golden-ratio expansion (the Ly-Algebra core). 2. An NTRU-style lattice public-key layer with trusted circulant-matrix inversion. 3. A symmetry-modulated padding that scales ciphertext noise according to the QEI of the supplied input vector. The result is a hybrid scheme in which low geometric coherence effectively raises the noise floor until decryption fails, and a deterministic hard gate rejects decryption entirely once QEI falls below a calibrated threshold. This yields both a probabilistic security bound and a deterministic anti-tamper mechanism. ======================================== 2. Preliminaries 2.1 Ly-Algebra and Quasi-Equivalence Index The Ly-Algebra is a five-dimensional graded construction whose product is defined by a mapping from integer matrices L_i over F_11 (or R) weighted by golden-ratio coefficients. Given an input vector v in R^5, the Quasi-Equivalence Index is computed as: QEI(v) = max(0, 1 - sigma_distortion / sigma_identity) where sigma_distortion is the weighted Euclidean norm of the graded square Lv. High QEI indicates that v lies close to the preferred symmetry locus of the algebra; low QEI indicates structural distortion. 2.2 NTRU-Style Lattice Layer The lattice component follows the classical NTRUEncrypt paradigm: - Private key: ternary polynomial f with controlled weight parameter d_f. - Public key: h = f^(-1) Ă g (mod q), where inversion is performed via the circulant matrix of f over Z/qZ. - Encryption: e = r Ă h + m (mod q). - Decryption: recover a = f Ă e (mod q), then multiply by the inverse of f modulo p and center to obtain m. The parameter set used in this work is n = 17, q = 2048, p = 3, d_f = 3 (a convenience configuration) with compressed configurations exploring the boundary of reliable recovery. ======================================== 3. TT-G41 Construction 3.1 Hybrid Architecture TT-G41 operates in two modes: - Pure Ly-Algebra mode: computes QEI and reports the result only. - NTRU-enhanced mode: performs full key generation, encryption, and decryption, optionally modulated by the supplied input vector. 3.2 Symmetry-Modulated Padding (Coupling Mechanism 3) When an input vector v is supplied at encryption, the system computes: s = exp(-alpha Ă QEI(v)) and adds deterministic noise of amplitude proportional to s to the message polynomial. The same vector (hence the same QEI) must be supplied at decryption to subtract the matching noise pattern. A mismatch leaves residual noise that destroys the plaintext. Two operating regimes are defined: - Hard mode (amplitude s Ă 1.8): produces active anti-tamper behavior. - Soft mode (amplitude s Ă 0.55): scientific characterization of the failure curve. 3.3 Hard Gate In production (hard mode), the decryption program first evaluates QEI. If QEI < 0.12, decryption is rejected with the exception: ValueError: structurally incoherent (QEI = ... < 0.12). Decryption rejected by hard gate. No partial plaintext is ever returned. This eliminates the common side-channel leak associated with error-correcting or soft-decision decoders. ======================================== 4. Empirical Security Bound A soft-diagnostic campaign of 4000 encrypt/decrypt trials was performed across a radial drift of the input vector that systematically lowers QEI. Failure probability was recorded at each point. Three models were fitted: Simple exponential: P(fail) = exp(-alpha Ă QEI), alpha = 3.612, R-squared = 0.945 Shifted exponential: P(fail) = exp(-alpha Ă max(QEI - q0, 0)), alpha = 23.55, q0 = 0.168, R-squared = 0.976 Logistic (best fit): P(fail) = (1 + exp[beta Ă (QEI - Q_mid)])^(-1), beta = 15.57, Q_mid = 0.209, R-squared = 0.980 The logistic model provides the highest fidelity. At the operational threshold QEI = 0.12, the mean observed failure rate is 0.963; above the threshold it falls to 0.323. The hard gate therefore sits safely on the high-failure shoulder of the empirically determined curve. ======================================== 5. Discussion The central claim of TT-G41 is that a continuous geometric invariant of a graded algebra can be turned into a practical cryptographic control surface. The symmetry-modulated padding realises a causal chain: geometric distortion -> elevated noise -> decryption failure while the hard gate converts the continuous measure into a binary, side-channel-resistant decision. Because the QEI is computed from a public or shared input vector, the anti-tamper property can be applied to any data source whose structural integrity is expected to remain high (sensor streams, physical-system state vectors, authenticated configuration parameters, etc.). A shift in that source immediately invalidates the cryptographic layer. Limitations of the present study include the modest lattice dimension (n = 17) used for the statistical campaign and the still-sharp transition of the underlying QEI landscape. Both are engineering parameters that can be refined without altering the architectural principle. ======================================== 6. Future Work and Research Directions Building upon the foundations established in this work, several promising extensions are identified for subsequent investigation: 6.1 Scaling Lie Algebra Dimensions The current construction relies on a five-dimensional Lie algebra. Exploring higher-dimensional Lie algebras, such as higher-rank semisimple algebras or structures analogous to E8, could provide a broader entropy space and create more complex geometric invariants for the Quasi-Equivalence Index. This would enhance the system's robustness against adversarial vector manipulation attacks. 6.2 Adapting the NTRU Layer to NIST Post-Quantum Standards The lattice dimension n = 17 was employed in the initial statistical campaign to explore operational boundaries. It is of significant interest to test how the logistic security bound behaves when scaling the NTRU layer to align with standard NIST dimensions, such as n = 503, 701, or 821, and to study whether the symmetry-modulated padding maintains computational efficiency at these substantially larger dimensions. 6.3 Adaptive Hard-Gate Thresholding Rather than relying on a fixed failure threshold at QEI = 0.12, an adaptive algorithm could be designed to dynamically adjust this threshold based on the statistical variance of the input vector stream. This extension would render the system suitable for Internet of Things applications or industrial control systems where natural structural noise levels vary over time. 6.4 Integration with Zero-Knowledge Proofs The geometric coherence represented by the Quasi-Equivalence Index could serve as the foundation for a novel zero-knowledge proof protocol. A prover could demonstrate possession of a structurally coherent vector without revealing the actual data, leveraging the continuous property of the geometric invariant as a geometric hash function. 6.5 Hardware Implementation and Side-Channel Analysis Implementing the hard-gate logic and symmetry-modulated padding mechanisms on FPGA platforms would enable evaluation of actual resistance to side-channel attacks, such as power consumption and electromagnetic emissions. The deterministic rejection of decryption may exhibit a unique power signature worthy of study to ensure no information leakage occurs via a side channel when the hard mode is activated. 6.6 Integration with Quantum Entropy Incorporating Quantum Random Number Generators into the symmetry-modulated padding mechanism would inject true quantum entropy into the noise vector, adding an additional layer of protection that directly bridges lattice-based cryptography and quantum mechanics. ======================================== 7. Conclusion TT-G41 demonstrates that Ly-Algebraic geometric coherence can be elevated from a passive diagnostic into an active post-quantum security primitive. The combination of
The modality ladder grades three results: triadic structure as theorem, a lit (self-knowing) ground as inference to best explanation, and personhood as free encounter. This paper closes the gap at the second rung by elimination rather than inference. Five independently earned steps: the Ground-Level Intent Trilemma (borrowed directedness requires regress, random directedness was already eliminated, only self-grounding survives); self-directed activity must track its target; subjectâobject identity at the ground removes the conditions for misrepresentation; the subjectâobject gap is shown to be the sole structural feature distinguishing accurate directedness from knowledge, with the candidate space closed under gap-dependence by the Zero Test; and the Distinguishability Lemma applied to Presence itself forces self-constituting Presence to be self-presenting, since ÎŚ is a mapping with intrinsic sourceâterminus structure. Zombie and normativity objections are addressed directly. The epistemic/volitional freedom distinction shows relational freedom survives the proof, leaving the third rung intact.
CHAIMAA EL HAMDANI, Jamaa LAMKIES, Zakaria ELMEKHLOUFI, Mohammed Kehel
RĂŠsumĂŠ : Objectif : Cet article examine dans quelle mesure le contrĂ´le de gestion territorial peut servir de levier de rĂŠsilience et d'agilitĂŠ organisationnelles dans les collectivitĂŠs territoriales marocaines, dans un contexte de contraintes budgĂŠtaires et institutionnelles. MalgrĂŠ l'abondance des travaux sur le pilotage de la performance publique, la rĂŠsilience et l'agilitĂŠ organisationnelles, la littĂŠrature n'offre Ă ce jour aucune articulation systĂŠmatique entre contrĂ´le de gestion territorial, rĂŠsilience et agilitĂŠ dans les collectivitĂŠs locales marocaines ; combler prĂŠcisĂŠment cette lacune constitue l'objet du prĂŠsent article. MĂŠthodologie : Il s'agit d'un article conceptuel (conceptual paper) fondĂŠ sur une revue narrative de la littĂŠrature indexĂŠe dans Scopus et sur une analyse documentaire des rĂŠformes institutionnelles marocaines (Constitution de 2011, loi organique relative aux lois de finances de 2015), complĂŠtĂŠe par une grille d'analyse comparative public-privĂŠ destinĂŠe Ă identifier les pratiques de gestion transfĂŠrables. Apports : L'article formule des propositions thĂŠoriques, et non des rĂŠsultats empiriques, selon lesquelles le contrĂ´le de gestion territorial peut renforcer la rĂŠsilience et l'agilitĂŠ par l'alignement stratĂŠgique, les systèmes de mesure de la performance, les mĂŠcanismes d'apprentissage organisationnel et les processus dĂŠcisionnels adaptatifs. L'analyse conceptuelle met en ĂŠvidence un ĂŠcart de maturitĂŠ significatif entre les dispositifs de contrĂ´le de gestion des secteurs privĂŠ et public, tout en montrant que le cadre constitutionnel de 2011 et la rĂŠforme budgĂŠtaire de 2015 offrent des conditions favorables Ă une gouvernance territoriale plus agile. Trois facteurs critiques de succès sont dĂŠgagĂŠs de la littĂŠrature : l'alignement stratĂŠgique du contrĂ´le de gestion avec les objectifs de dĂŠveloppement local, l'investissement dans le capital humain et les infrastructures numĂŠriques, et une hybridation progressive et sĂŠlective des outils privĂŠs adaptĂŠe aux valeurs du service public. Implications : L'ĂŠtude propose aux dĂŠcideurs territoriaux des recommandations hiĂŠrarchisĂŠes selon leur horizon de mise en Ĺuvre et identifie des pistes de validation empirique du modèle proposĂŠ. OriginalitĂŠ : La contribution de l'article rĂŠside dans l'intĂŠgration, au sein d'un cadre analytique unifiĂŠ, de trois corpus jusque-lĂ juxtaposĂŠs, appliquĂŠe au contexte des collectivitĂŠs territoriales marocaines et transposable avec prudence Ă des contextes de dĂŠcentralisation comparables. Mots clĂŠs : ContrĂ´le de gestion territorial, rĂŠsilience organisationnelle, agilitĂŠ organisationnelle, collectivitĂŠs territoriales, Nouveau management public, Maroc. Classification JEL : H70, H83, L38, M48 Type du papier : Recherche ThĂŠorique Abstract : Purpose: This paper examines the extent to which territorial management control can serve as a lever for organizational resilience and agility in Moroccan local governments facing budgetary and institutional constraints. Despite a growing body of literature on public sector performance management, organizational resilience, and organizational agility, no systematic articulation between territorial management control, resilience, and agility has been proposed for Moroccan local governments; addressing this precise gap constitutes the purpose of this study. Design/methodology/approach: The article is a conceptual paper based on a narrative review of Scopus-indexed literature and a documentary analysis of Moroccan institutional reforms (2011 Constitution, 2015 Organic Law on Finance Laws). A comparative public-private analytical grid is used to identify transferable management practices. Findings: The study derives theoretical propositions rather than empirical results. It suggests that territorial management control may enhance resilience and agility through strategic alignment, performance measurement systems, organizational learning mechanisms, and adaptive decision-making processes. The conceptual analysis points to a significant maturity gap between private and public sector management control systems, while indicating that the 2011 Constitution and the 2015 budget reform provide enabling conditions for more agile territorial governance. Three critical success factors are inferred from the literature: strategic alignment of management control with local development objectives, investment in human capital and digital infrastructure, and progressive, selective hybridization of private-sector tools adapted to public service values. Originality/value: The contribution of the paper lies in integrating three previously separate bodies of literature within a unified analytical framework applied to Moroccan local governments, with cautious transferability to comparable decentralizing contexts. Practical implications: The study offers local decision-makers recommendations prioritized by implementation horizon and identifies avenues for future empirical validation of the proposed model. Keywords: Territorial management control, organizational resilience, organizational agility, local government, New Public Management, Morocco. JEL Classification: H70, H83, L38, M48 Paper type: Theoretical Research
This study aims to analyze the comparative performance of Cryptocurrency Bitcoin, Stocks, and Gold as investment instruments using performance measurement variables including the Sharpe, Treynor, Jensen, and Sortino ratios. This research employs a descriptive quantitative approach. The population consists of monthly closing prices of Bitcoin, IDX30 stocks, and Gold. The sampling technique used is saturated sampling, resulting in 60 data points for each investment instrumentâBitcoin, IDX30 stocks, and Goldâduring the period from January 1, 2020 to December 31, 2024.The analytical method applied is comparative analysis using secondary data. The data were initially calculated using Microsoft Excel and subsequently processed statistically using SPSS through the KruskalâWallis test. The results indicate significant differences among Bitcoin, IDX30 stocks, and Gold when investment performance is assessed using the Sharpe, Treynor, Jensen, and Sortino indices.Based on the KruskalâWallis test, Gold demonstrates the best performance according to the Sharpe and Jensen indices, IDX30 stocks perform best according to the Treynor index, and Bitcoin shows the best performance according to the Sortino index. However, based on the highest overall average return value, Cryptocurrency Bitcoin outperforms the other instruments. Therefore, it can be concluded that the best alternative investment is Cryptocurrency Bitcoin. Penelitian ini bertujuan untuk menganalisis perbandingan kinerja Cryptocurrency Bitcoin, Saham, dan Emas sebagai instrumen investasi menggunakan variabel pengukuran kinerja Sharpe, Treynor, jensen, dan Sortino. Jenis penelitian merupakan kuantitatif Deskriptif. Populasi yang digunakan merupakan harga penutupan bulanan dari Bitcoin, IDX30, dan Emas. Teknik pemilihan sampel adalah sampel jenuh yang berjumlah 60 data untuk masing-masing instrumen investasi Bitcoin, Saham IDX30, dan Emas selama periode 1 januari 2020 â 31 Desember 2024. Metode analisis yang digunakan adalah metode komperatif dan menggunakan data sekunder. Data dihitung terlebih dahulu dengan menggunakan program Microsoft Excel kemudian diolah secara statistik menggunakan aplikasi SPSS yaitu Uji Kruskall-Wallis. Hasil penelitian menunjukkan bahwa terdapat perbedaan yang signifikan antara Bitcoin, Saham IDX30, dan Emas jika kinerja investasi dilihat dari Indeks Sharpe, Treynor, Jensen, dan Sortino. Berdasarkan uji Kruskal-Wallis, investasi terbaik menurut indeks Sharpe dan Jensen , adalah Emas, sedangkan menurut Indeks Teynor Saham IDX30, dan menurut Indeks Sortino adalah Cryptocurrency Bitcoin. Sedangkan dengan nilai jumlah rata-rata terbaik adalah Cryptocurrency Bitcoin. Sehingga dapat disimpulkan bahwa alternatif investasi terbaik adalah Cryptocurrency Bitcoin.
The Ăbermensch Guard â PRE-GHR XIV. v2.3 (2026-08-09): post-publish review fixes (v2.2 shipped, then corrected). Changes vs v2.2: (1) §2.2 pairing frame compressed to one sentence â "The pairing is structural, not ideological; the extent of their disagreement is addressed in §2.3" â eliminating duplication with §2.3's non-composition paragraph (same contrast, same conclusion, near-identical wording); the full contrast now lives once, at §2.3. (2) Changelog cleaned: the v2.2 entry's Chinese parenthetical removed; review-count wording aligned with agent_note (four independent AI stress-test reviews). v2.2 (2026-08-09): four independent AI stress-test reviews; the author retained final judgment, accepting two must-fix items and rejecting the rest. Changes vs v2.1: (1) abstract opens with "This paper is not an AGI alignment solution"; PRE-GHR downgraded from "scientific scaffolding" to "one possible engineering interpretation â an instantiation candidate, not its foundation" across abstract, §5, §8. (2) §2.3 corrected: the two limits emerge between, not intersect at â the earlier "intersection" wording contradicted the same section's "refuse to merge"; between/space language adopted. (3) Two Demons qualified as philosophical boundary conditions, not claims of physical unification (abstract, §2). (4) §2.2 framed the NietzscheâKorchagin pairing as structural, not ideological â the weld is declared, not reconciled. (5) §1 early declaration: the paper is not an attempt to align AGI with Nietzsche's ethics â it guards the question against being answered badly. (6) §2.3 new paragraph: the ledger's bills are not distributed symmetrically; constraint is the non-externalization clause â the boundary right of the weak and the constraint on the strong are the same clause, read from opposite sides (series interface with the Sender Axiom line, drawn in this paper's own terms). (7) Compression: §3, §4, §6, §8 tightened (~13 lines cut); measured net body length +5.2% â review-requested strengthenings outweigh the cuts; no cuts to passages reviews themselves praised (Korchagin framing, §9 posture). v2.1 (2026-08-08): stress-test review fixes (§2.1 physics corrected â quantum fails the demon at the level of knowing, chaos at the level of computing; §2.3 Two Demons' non-composition declared explicitly; §1 dual failure mode: power without wisdom OR the last man's weakness dressed as virtue). v2L (2026-08-08): manifestationâtest reframe; entity/direction correction; eternal-recurrence mapping withdrawn; Nazi-reception history made honest; Two-Demons framing added (Laplace/Nietzsche cognitive limit; Maxwell/Korchagin action limit; Landauer shared ledger). Series: PRE-GHR XIV. License CC-BY-4.0.
** Autonomous Computational Law with StellarEq and ACRPL Model ** To address the systemic vulnerabilities of legacy natural-language governanceâspecifically its semantic ambiguity, high-latency auditability, and susceptibility to centralizationâthis paper presents a mathematically formalized, dual-engine architecture for Autonomous Computational Law under the Computable Political Language (CPL) stack, proving topological boundary-enforcement and stability via sheaf theory, homological algebra, and Lyapunov optimization. Systemic resource allocation and dynamic authority routing are governed by the Stellar Causal Power Flow (SCPF) engine, which proves state-transition convergence strictly based on the fundamental axiom of political energetics: $$\text{Power}(t) = \text{Contribution}(t) \times \text{AdoptionRate}(t)$$ Within this architecture, the mathematically rigorous constraints of our formal legal framework continuously generate decentralized trust, naturally shielding the vulnerable systemic core from coercive, extractive authority. By harnessing these parameters, the fluid and dynamic flow of distributed contributions cultivates a sprawling forest of policy proposals, smoothly transforming raw physical effort into radiant social energy to illuminate civilizational evolution. To maintain absolute structural integrity, an uncompromised cryptographic protocol strictly curtails the unchecked growth of algorithmic outputs, preventing the multidimensional essence of human rights from collapsing into scalar tradeable variables. Furthermore, persistent algorithmic decay systematically cools the high-temperature transactional friction of the marketplace, recursively returning accumulated power back to the common reservoir of collective sovereignty. Discrete logic boundary enforcement is handled ex-ante by the ACRPL, which defines non-negotiable constitutional safeguards as mathematical predicates over a non-convex feasible solution space, ensuring that no optimization gradient from the SCPF engine may enter the ledger unless the security gates are strictly satisfied, thus completely hiding compilation mechanics and specific variable transitions from unauthorized reconstruction.
PhiGraph Core 4.1.0-rc.6 is a model-agnostic governance system for software-agent and AI operations. This v2 draft extends the Zenodo v1 paper with a scoped transactional ledger (declared write locks, fail-closed verify_scoped_chain on JSON/SQLite), GRDI 0.4.0 shadow decision chain (envelope through replay audit, no external execution), and updated evaluation (319 automated tests at main@a5a7187). It retains the typed protocol, policy-gated runtime, HAV v0.2 fail-closed verification, and the bounded CIC-IDS2017 experiment with explicit limitations. Paper source is licensed CC BY 4.0. PhiGraph software is distributed separately under the repository software license. Git pin for this draft: a5a7187.
Muhammad Tauseef Tariq Kisana, Imran Ul Haq Imran Ul Haq
The pre- and post-harvest interface represents a critical control zone in the food supply chain where microbial contamination can compromise the safety, quality, and shelf-life. Although numerous studies have examined pre-harvest contamination and post-harvest disease management separately, limited reviews have comprehensively addressed the critical interface linking these stages. This review synthesizes current knowledge on contamination pathways, major pathogens, monitoring approaches, and integrated control strategies associated with fresh fruits and vegetables. Relevant peer-reviewed literature was critically evaluated to provide an overview of food safety risks and management options across the production chain. Fresh produce may become contaminated through soil, irrigation water, organic amendments, wildlife, harvesting equipment, storage environments, and human handling. These pathways facilitate the introduction and dissemination of bacterial, fungal, and viral pathogens. Major bacterial hazards include Salmonella enterica, Escherichia coli O157, and Listeria monocytogenes, while fungal pathogens such as Aspergillus, Fusarium, and Penicillium species contribute to spoilage and mycotoxin production. Viral pathogens, particularly norovirus and hepatitis A virus, are also important causes of produce-associated outbreaks. Environmental stressors, including drought, heavy rainfall, temperature fluctuations, and crop injuries, further increase contamination risks. The review highlights integrated management strategies, including Good Agricultural Practices (GAPs), sanitation programs, rapid cooling, biological control agents, and emerging decontamination technologies. Among these, ozone and cold plasma show strong antimicrobial potential, although their large-scale adoption is constrained by economic and technical limitations. Environmental Monitoring Programs (EMPs), molecular detection tools such as polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), and next-generation sequencing (NGS), together with blockchain-based traceability systems, support rapid pathogen detection and outbreak prevention. Integrating the Food Safety Objective (FSO) framework with One Health principles provides a sustainable approach for reducing contamination risks throughout the supply chain. Future research should focus on improving the cost-effectiveness, scalability, and practical implementation of emerging monitoring and intervention technologies. Keywords: Pre-harvest, post-harvest, food safety, fresh produce, microbial contamination, mycotoxins, biofilms, traceability, good agricultural practices, environmental monitoring programs.
Multi-heterogeneous power data in smart grid refers to power data that includes multiple types, modalities, and sampling frequencies, such as user electricity consumption, equipment operation, and grid scheduling. The diverse and heterogeneous power data in the smart grid is related to the stable operation of the grid and user privacy. Without effective protection, it is easy to cause risks such as information leakage and scheduling failure. Therefore, targeted security protection solutions need to be constructed. However, there are problems with the loss of information granularity, high risk of privacy leakage, and limited data analysis in the current smart grid power data aggregation and sharing. To enhance the security protection effect of power data, a multi-dimensional data security protection scheme based on data aggregation and Paillier homomorphic encryption is proposed. Firstly, a three-tier system model for multivariate heterogeneous power data in smart grids is constructed (smart meters, data collection stations, and blockchain nodes). Subsequently, the Paillier homomorphic encryption algorithm is integrated to encrypt and aggregate usersâ multi-dimensional electricity consumption data. At the same time, data aggregation and consortium chain technology have been introduced. Experimental results demonstrate that this scheme offers significant advantages over traditional Rivest-Shamir-Adleman (RSA) encryption schemes, traditional Advanced Encryption Standard (AES) encryption schemes, Elgamel encryption schemes, and traditional Transmission Control Protocol/Message Queuing Telemetry Transmission Protocol transmission schemes in terms of computational and communication overhead. When the number of users reaches 5000, the computational overhead at data collection stations is only 35.6% of that in traditional RSA methods, and the communication overhead is merely 28.7% of traditional transmission control protocol methods. Additionally, when transmitting power data from 5000 users simultaneously, the information accuracy rate exceeds 92%, and the packet loss rate remains below 0.5%. In conclusion, the proposed scheme provides an efficient and reliable technical pathway for the secure transmission of multivariate heterogeneous power data in smart grids.
This study investigates the multidimensional impacts of the Industry 5.0 paradigm on logistics and supply chain management, focusing primarily on human-centric digitalization and sustainability dynamics. Within this scope, aspects of operational efficiency, resilience, and financial performance are analysed as complementary dimensions within the thematic synthesis. Emphasising human-centric digitalization, it examines how the integration of advanced technologies with social responsibility principles reshapes supply chain strategies, fosters organisational transformation, and creates competitive advantages in the context of sustainable development. Following the PRISMA protocol, and using the Web of Science Core Collection as the primary database, a systematic literature review of 47 peer-reviewed studies was conducted, mapping thematic linkages among digitalization, resilience, financial outcomes, and sustainability. The synthesis identifies a conceptual framework that positions humanâmachine collaboration as a central enabler for sustainable transformation, enhancing decision-making, adaptability, energy efficiency, carbon footprint reduction, green innovation, and financial outcomes. Findings highlight interconnected pathways through which digitalization generates both operational gains and long-term strategic resilience. This study contributes an original analytical lens that unites human-centric digitalization, sustainability, resilience, and financial performance within a single framework, offering actionable insights for aligning technological innovation with sustainable supply chain strategies. In particular, the study points to practical pathways such as the use of digital twins for resource optimisation, blockchain for supply chain transparency, and AI-driven solutions for emission reduction.
The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.
Artificial Intelligence (AI) is reshaping contemporary fashion by transforming design processes, production systems, and sustainability strategies in the textile and apparel sector. Amid growing concerns over overconsumption, environmental degradation, carbon emissions, and social inequities, AI has emerged as both a technological enabler and a subject of ethical scrutiny. This study examines the influence of AI on creative practice, circular design implementation, and responsible innovation in fashion. Drawing on a qualitative synthesis of systematic literature, design theory, and industry case analyses, this study proposes a framework that situates AI within sustainable fashion discourse. Findings indicate that generative design tools, virtual prototyping, digital twins, and predictive analytics support waste reduction, virtual sampling, demand-responsive production, and informed material selection. AI-enabled resale systems, automated textile sorting, and blockchain-based traceability strengthen circular economy initiatives by extending product lifecycles and improving transparency. However, algorithmic decision-making challenges authorship, craftsmanship, dataset neutrality, and labor structures. Concerns over bias, intellectual property ambiguity, digital energy consumption, and workforce displacement complicate sustainability narratives. The study argues that sustainable transformation requires a human-centered governance approach in which AI augments rather than replaces creative agency and is supported by ethical regulation and critical design education. By integrating sustainability theory, computational creativity, and AI ethics, this research contributes a holistic framework for responsible AI adoption in fashion systems.
Penelitian ini bertujuan untuk menganalisis perkembangan literatur mengenai pemanfaatan teknologi digital dalam meningkatkan kualitas audit melalui pendekatan bibliometrik. Data penelitian diperoleh dari database Scopus dengan menggunakan kata kunci yang berkaitan dengan audit quality, auditing, dan berbagai teknologi digital. Setelah melalui proses penyaringan berdasarkan periode publikasi, bidang ilmu, jenis dokumen, dan bahasa, diperoleh 435 artikel yang diterbitkan pada periode 2011â2025. Analisis dilakukan menggunakan Biblioshiny pada paket Bibliometrix di R Studio untuk memetakan tren publikasi, sumber publikasi utama, penulis paling berpengaruh, jaringan kolaborasi, serta perkembangan tema penelitian. Hasil penelitian menunjukkan bahwa jumlah publikasi mengalami pertumbuhan yang signifikan dengan dominasi tema artificial intelligence, blockchain, big data analytics, dan machine learning. Journal of Emerging Technologies in Accounting dan International Journal of Accounting Information Systems menjadi sumber publikasi utama, sedangkan Vasarhelyi M.A. merupakan penulis paling berpengaruh dalam bidang ini. Analisis tematik menunjukkan bahwa audit quality masih berada pada kategori emerging themes, yang mengindikasikan perlunya penelitian lebih lanjut mengenai dampak teknologi digital terhadap kualitas audit. Temuan ini memberikan gambaran komprehensif mengenai evolusi penelitian audit berbasis teknologi dan peluang pengembangan penelitian di masa depan.