Rismi A. Dukomalamo Dukomalamo, Asrudin Hormati, Irfan Zam Zam
Abstract:This research was conducted in local governments in North Maluku province. The objective of this study is to examine the impact of independent variables, specifically financial performance measured by the degree of fiscal decentralization, regional financial dependence, SiLPA financing level, and regional financial efficiency, on the dependent variable, which is capital expenditure. This study used the technique of multiple linear regression. The findings of this study suggest that the degree of fiscal decentralization, regional financial dependence, level of SiLPA financing, and regional financial efficiency all have a simultaneous and significant impact on the allocation of capital expenditure. Specifically, the degree of fiscal decentralization and regional financial efficiency have a positive and significant effect on capital expenditure allocation, while regional financial dependence and the level of SiLPA financing do not have any effect on capital expenditure allocation. Keywords: regional financial performance, capital expenditure Abstrak:Penelitian ini dilakukan di pemerintah daerah di provinsi Maluku Utara. Tujuan dari penelitian ini adalah untuk menguji pengaruh variabel independen, khususnya kinerja keuangan yang diukur dengan derajat desentralisasi fiskal, ketergantungan keuangan daerah, tingkat pembiayaan SiLPA, dan efisiensi keuangan daerah, terhadap variabel dependen, yaitu belanja modal. Penelitian ini menggunakan teknik regresi linier berganda. Temuan dari penelitian ini menunjukkan bahwa derajat desentralisasi fiskal, ketergantungan keuangan daerah, tingkat pembiayaan SiLPA, dan efisiensi keuangan daerah secara simultan dan signifikan berpengaruh terhadap pengalokasian belanja modal. Secara spesifik, derajat desentralisasi fiskal dan efisiensi keuangan daerah berpengaruh positif dan signifikan terhadap alokasi belanja modal, sedangkan ketergantungan keuangan daerah dan tingkat pembiayaan SiLPA tidak berpengaruh terhadap alokasi belanja modal. Kata kunci : Kinerja Keuangan Daerah, Belanja modal.
Nigeria faces an urgent energy challenge marked by chronic electricity shortages, dependence on fossil fuels, and worsening environmental degradation. This study examines the economic and environmental benefits of transitioning to renewable energy in Nigeria, adopting a mixed-methods approach that combines a systematic literature review, policy analysis, and synthesis of empirical case studies. Findings reveal that Nigeria possesses vast potential for solar, wind, biomass, and hydropower, capable of transforming its energy landscape. Economically, the adoption of renewable energy can generate employment, stimulate industrial growth, expand rural electrification, attract investment, and stabilize public finances by reducing vulnerability to global oil price shocks. Environmentally, it can reduce greenhouse gas emissions, improve air quality, conserve biodiversity, promote sustainable waste management, and enhance resilience to climate variability. Case studies demonstrate the effectiveness of decentralized solar mini-grids, biomass utilization, and hybrid systems in meeting local energy needs; however, persistent barriers, including weak policy enforcement, financing gaps, and infrastructural limitations remain. The study concludes that a comprehensive framework is required, built on policy alignment, financing innovation, institutional strengthening, infrastructure development, and social inclusion. Renewable energy transition thus represents not only a climate responsibility but also a strategic pathway for Nigeria’s sustainable economic and environmental future.
Lana AL-Khalaileh, Tareq Al-Billeh, Abdul Salam Al-Findi, Odai Al-Hailat
This study deals with a new technology in contracting, resulting from the information technology (IT) revolution in the field of electronic transactions, which is called “smart contracts”. The latter has constituted a breakthrough in the field of contracting since it provides automation, which underlies many advantages for contractors, so that the software works of smart contracts provide immediate and automatic execution of the contract, which provides speed of implementation and security from manipulation after concluding the contract. So, it provides elements of technical security and trust for this type of contract. This new contractual pattern is considered one of the first in the provisions of Islamic Sharia, which urges us to know the extent of its compatibility with its contracting system. The study concluded with several recommendations, the most significant being that international accords lack comprehensive legislation governing transactions executed through smart contracts. While they contain certain restrictions about contracts formed through contemporary electronic methods, they inadequately elucidate the characteristics of such contracts and examine their specifics. The legal issues associated with smart contracts stem from their connection to digital currency, which is banned by Sharia law.
Smart contracts have emerged as key components within decentralized environments, enabling the automation of transactions through self-executing programs. While these innovations offer significant advantages, they also present potential drawbacks if the smart contract code is not carefully designed and implemented. This paper investigates the capability of large language models (LLMs) to detect OWASP-inspired vulnerabilities in smart contracts beyond the Ethereum Virtual Machine (EVM) ecosystem, focusing specifically on Solana and Algorand. Given the lack of labeled datasets for non-EVM platforms, we design a synthetic dataset of annotated smart contract snippets in Rust (for Solana) and PyTeal (for Algorand), structured around a vulnerability taxonomy derived from OWASP. We evaluate LLMs under three configurations: prompt engineering, fine-tuning, and a hybrid of both, comparing their performance on different vulnerability categories. Experimental results show that prompt engineering achieves general robustness, while fine-tuning improves precision and recall on less semantically rich languages such as TEAL. Additionally, we analyze how the architectural differences of Solana and Algorand influence the manifestation and detectability of vulnerabilities, offering platform-specific mappings that highlight limitations in existing security tooling. Our findings suggest that LLM-based approaches are viable for static vulnerability detection in smart contracts, provided domain-specific data and categorization are integrated into training pipelines.
Cross chain interoperability in blockchain systems exposes a fundamental tension between user privacy and regulatory accountability. Existing solutions enforce an all or nothing choice between full anonymity and mandatory identity disclosure, which limits adoption in regulated financial settings. We present VeilAudit, a cross chain auditing framework that introduces Auditor Only Linkability, which allows auditors to link transaction behaviors that originate from the same anonymous entity without learning its identity. VeilAudit achieves this with a user generated Linkable Audit Tag that embeds a zero knowledge proof to attest to its validity without exposing the user master wallet address, and with a special ciphertext that only designated auditors can test for linkage. To balance privacy and compliance, VeilAudit also supports threshold gated identity revelation under due process. VeilAudit further provides a mechanism for building reputation in pseudonymous environments, which enables applications such as cross chain credit scoring based on verifiable behavioral history. We formalize the security guarantees and develop a prototype that spans multiple EVM chains. Our evaluation shows that the framework is practical for today multichain environments.
Maneesha Papireddygari, Xintong Wang, Bo Waggoner, David M. Pennock
Automated Market Makers (AMMs) are used to provide liquidity for combinatorial prediction markets that would otherwise be too thinly traded. They offer both buy and sell prices for any of the doubly exponential many possible securities that the market can offer. The problem of setting those prices is known to be #P-hard for the original and most well-known AMM, the logarithmic market scoring rule (LMSR) market maker [Chen et al., 2008]. We focus on another natural AMM, the Constant Log Utility Market Maker (CLUM). Unlike LMSR, whose worst-case loss bound grows with the number of outcomes, CLUM has constant worst-case loss, allowing the market to add outcomes on the fly and even operate over countably infinite many outcomes, among other features. Simpler versions of CLUM underpin several Decentralized Finance (DeFi) mechanisms including the Uniswap protocol that handles billions of dollars of cryptocurrency trades daily. We first establish the computational complexity of the problem: we prove that pricing securities is #P-hard for CLUM, via a reduction from the model counting 2-SAT problem. In order to make CLUM more practically viable, we propose an approximation algorithm for pricing securities that works with high probability. This algorithm assumes access to an oracle capable of determining the maximum shares purchased of any one outcome and the total number of outcomes that has that maximum amount purchased. We then show that this oracle can be implemented in polynomial time when restricted to interval securities, which are used in designing financial options.
Distributed-ledger technologies (DLTs) have upended the design logic of, data-sharing web architectures, especially within sectors that demand uncompromising transparency, indelible audit trails, and decentralised governance. Yet curating an optimal DLT stack remains an intricate optimisation puzzle involving nuanced trade-offs across cryptographic rigour, elastic scalability, experiential ergonomics, propagation latency, cross-ledger interoperability, and fiscal prudence. To navigate this complexity, we introduce a tiered decision-support framework that welds expert-elicited priorities to empirical performance signals within a rigorous multi-criteria outranking model. The scheme yields transparent, rank-ordered shortlists of candidate ledgers and is demonstrated across healthcare, fintech, and supply-chain provenance scenarios. Results confirm the model’s ability to surface context-specific “best fits” even when decision objectives clash, thereby equipping engineers, CIOs, and policy designers with a defensible roadmap for trustworthy, efficient, and governance-aligned blockchain adoption. Future iterations will embed fuzzy logic and live-telemetry feedback to sharpen responsiveness in rapidly evolving operating environments.
This study employs a theoretical, system design–based methodology to propose the Palm GreenChain framework—a blockchain-based platform aimed at enhancing traceability, transparency, financial coverage, and accountability in green bond financing for sustainable palm oil production in Malaysia. The methodology integrates Ethereum-compatible smart contracts, ESG oracles, IPFS-based data storage, and DAO (Decentralized Autonomous Organization) governance to structure a digital green bond lifecycle. Rather than relying on empirical data collection, the framework is conceptualized through the development of a multi-layered blockchain architecture and validated via comparative analysis with analogous blockchain applications in agriculture. The proposed system is designed to enable real-time traceability of green bond disbursements, automate ESG compliance verification using satellite and IoT data, and strengthen accountability and access to climate finance for smallholder farmers. By embedding performance-based returns within smart contracts, the model aligns financial incentives with conservation goals. Leveraging Malaysia’s advanced land administration infrastructure and digital capabilities, the framework presents a scalable, open-source solution to reduce greenwashing, expand financial inclusion in underserved agricultural communities, and enhance transparency and investor confidence in sustainable agricultural finance. By directly linking green finance to verifiable sustainability outcomes, Palm GreenChain addresses key limitations in conventional green bond mechanisms. Its applicability across diverse agricultural sectors positions it as a replicable blueprint for broader sustainable development. The framework is openly available via its GitHub repository.
The relevance of the study is determined by the need for in-depth study and systematization of innovative decision-making methods that Web3 technologies offer to the modern business environment. In the context of global digital transformation, traditional approaches to management and finance are proving insufficient to ensure the competitiveness and sustainable development of organizations. The purpose of this article is to analyze Web3 tools, in particular blockchain, asset tokenization, decentralized finance (DeFi), and decentralized autonomous organizations (DAOs), as a basis for forming new, more transparent, secure, and effective methods and models for management decision-making. The paper applies a comprehensive methodology that includes a systematic analysis of the functional capabilities of Web3 technologies and a structural-logical approach to classifying their impact on corporate governance and financial management. The use of case studies has made it possible to illustrate the practical aspects of integrating these tools into the activities of large companies. The results confirm that Web3 is not only a technological trend but also a new paradigm that provides managers with qualitatively different tools. It has been established that blockchain creates a foundation for trust and data security; tokenization and DeFi radically increase the flexibility and liquidity of financial management; and DAOs transform corporate governance into a collective and inclusive process. In addition, the integration of AI agents into routine operations allows managers to effectively refocus their attention on strategic planning. The practical value of the article lies in providing organizations with clear recommendations for implementing Web3 technologies: from the need to start with pilot projects to test systems and processes to the mandatory investment in the development of internal competencies. The materials in the article can serve as a basis for developing innovative strategies that will help business organizations minimize technical and regulatory risks and secure leadership in today's digital market.
In this interview with Ana Maria Caballero, we explore how poetry intersects with technology and Web3, highlighting its potential to redefine creative expression, challenge power dynamics, and enhance the cultural relevance of poetry in the digital age.
This context informs the conversations with artists and creative practitioners in this book.Often, their work with and around Decentralised Autonomous Organisations (DAOs) emerges from these very concerns.Does that mean that DAOs are capable of solving the sociopolitical issues of precarity, cuts, and censorship?No. 'Free blockchain money' does not exist.DAOs do not 'magically' make more funding appear, least of all structurally so.And DAOs do not allow artists and cultural practitioners to leave behind their national contexts of austerity and repression and exchange them for some virtual utopia.However, this does not mean that engaging with DAOs is pointless in the face of these circumstances and limitations.In this publication, I ask practitioners to share their experiences, focussing specifically on the definition of new forms of agency in cultural decision-making, explorations of shared ownership in arts and culture amid widespread logics of private property and extractivism, and the making of prefigurative claims on futures envisioned from the bottom up.None of these practices will be able to replace the structures of state funding or cancel oppressive concentrations of power any time soon, but they do open up space to manoeuvre and create tactical interventions, to find each other and build solidarity, and to regain a sense of futurity together.In other words, to reimagine, reclaim, and restructure shared socio-technical futures.The six people that I interviewed represent key voices in the countercultural and artistic DAO space.Penny Rafferty is a cofounder of Black Swan -a DAO that pursued horizontal and decentralised approaches to art-making -and she pushes DAO discourse into new directions with critical and imaginative work.Erik Bordeleau is a co-founder of The Sphere -a DAO that explores new ecologies of funding to develop a regenerative commons for the performing arts -and contributes boundarypushing philosophical and media theoretical perspectives to DAO thought.Ruth Catlow developed CultureStake -a voting system for decentralised cultural decision-making that uses quadratic voting on the blockchain -and has been a central Repression of Palestinian Culture and soidarity: Independence as Resistance,' Reset! 8
Lingfei Qian, Xueqing Peng, Yan Wang, Vincent Jim Zhang · 17 authors
Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies test models instead of agents, cover limited periods and assets, and rely on unverified data. To address these gaps, we introduce Agent Market Arena (AMA), the first lifelong, real-time benchmark for evaluating LLM-based trading agents across multiple markets. AMA integrates verified trading data, expert-checked news, and diverse agent architectures within a unified trading framework, enabling fair and continuous comparison under real conditions. It implements four agents, including InvestorAgent as a single-agent baseline, TradeAgent and HedgeFundAgent with different risk styles, and DeepFundAgent with memory-based reasoning, and evaluates them across GPT-4o, GPT-4.1, Claude-3.5-haiku, Claude-sonnet-4, and Gemini-2.0-flash. Live experiments on both cryptocurrency and stock markets demonstrate that agent frameworks display markedly distinct behavioral patterns, spanning from aggressive risk-taking to conservative decision-making, whereas model backbones contribute less to outcome variation. AMA thus establishes a foundation for rigorous, reproducible, and continuously evolving evaluation of financial reasoning and trading intelligence in LLM-based agents.
Carlo Brunetta, Amit Chaudhary, Stefano Galatolo, Massimiliano Sala
Dynamically distributed inflation is a common mechanism used to guide a blockchain's staking rate towards a desired equilibrium between network security and token liquidity. However, the high sensitivity of the annual percentage yield to changes in the staking rate, coupled with the inherent feedback delays in staker responses, can induce undesirable oscillations around this equilibrium. This paper investigates this instability phenomenon. We analyze the dynamics of inflation-based reward systems and propose a novel distribution model designed to stabilize the staking rate. Our solution effectively dampens oscillations, stabilizing the yield within a target staking range.
The blockchain technology is sweeping the globe. Blockchain has emerged as a disruptive technology for the future generation of multiple industrial applications because to its decentralised, transparent, and secure nature. Cloud of Things, which is possible by the marriage of cloud computing with the Internet of Things, is one of them. Considering the need for security and efficiency as a problem, this paper proposes a safe and efficient smart home design that combines blockchain and cloud computing technologies to provide a comprehensive solution. The decentralised nature of blockchain technology allows it to provide processing services and create transaction copies of obtained sensible user data from smart homes. Blockchain, a distributed ledger technology that provides an immutable log of transactions recorded on a distributed network, has lately gained popularity as the underlying technology of cryptocurrencies and is revolutionising data storage and processing in computer network systems. Blockchain is seen as a possible option for future data-driven networks (DDNs) to provide safe data storage, sharing, and analytics, user privacy protection, strong, trustworthy network governance, and decentralised routing and resource management
The rapid growth of data-driven applications in healthcare, finance, IoT, and autonomous systems has created a pressing need for privacy-preserving and scalable machine learning methods. Traditional centralized learning, which aggregates data into a single repository, faces challenges related to data privacy, security, communication overhead, and regulatory compliance. Federated Learning (FL) offers a decentralized solution, enabling multiple clients to collaboratively train a global model without sharing raw data. Only model updates are exchanged, preserving privacy while leveraging distributed computational resources. This paper reviews FL architectures— including centralized, decentralized, horizontal, vertical, cross-device, and cross-silo—along with core components such as local clients, central servers, and communication protocols. Privacy- preserving techniques like differential privacy, secure aggregation, homomorphic encryption, and anonymization/pseudonymization are discussed to protect sensitive information. FL applications span healthcare, finance, IoT, smart devices, and autonomous systems, highlighting its transformative potential. Key challenges include data and system heterogeneity, efficient aggregation, personalization, robustness, and regulatory compliance. Future directions focus on enhanced privacy, communication efficiency, model personalization, and integration with edge and IoT environments. FL thus represents a promising paradigm for secure, collaborative, and distributed artificial intelligence.
This study aims to understand the relationship among cryptocurrency, stock, and gold markets. Cointegration, structured VAR, and causality tests were used with daily datasets from 11/09/2017 to 11/17/2023. A cryptocurrency basket is accepted as the cryptocurrency market for this study. The stock markets have a one-way relationship both with the gold and cryptocurrency markets in the short-run. All markets have effects on other markets’ price variances, as well. The price shocks of the markets to each other are not so essential for the prices. However, their own price shocks impact their prices for a few days. The stock market has asymmetric relationships with the gold and cryptocurrency markets. A 1.00 % rise in stock price causes declines in the gold and cryptocurrency prices by 2.35% and 2.42%, respectively. If the gold market or stock market is ignored, a 1.00% rise in gold prices causes a 0.69% rise in cryptocurrency prices, or a 1.00% rise in stock prices raises the cryptocurrency prices by 4.03%.
Cryptocurrency investment is a rapidly growing financial sector, marked by high volatility, decentralized technologies, and significant profit potential. Investors use strategies like long-term holding (“HODLing”), portfolio diversification, and short-term trading. “HODLing” relies on long-term value appreciation but requires resilience to price fluctuations. Diversifying with assets like Bitcoin and Ethereum reduces risk due to their low correlation with traditional investments. The crypto market is highly sensitive to geopolitical, economic, and technological factors, attracting investors during economic instability. Advanced models like LASSO and AutoEncoder aid in price prediction and strategy optimization. Despite high return potential, careful risk management is essential due to volatility and regulatory uncertainty. This study experimentally applies identical cryptocurrency portfolios to different investment strategies, identifying the most profitable approach.
Jonas Gebele, Timm Mutzel, Burak Oez, Florian Matthes
Sealed-bid auctions ensure fair competition and efficient allocation but are often deployed on centralized infrastructure, enabling opaque manipulation. Public blockchains eliminate central control, yet their inherent transparency conflicts with the confidentiality required for sealed bidding. Prior attempts struggle to reconcile privacy, verifiability, and scalability without relying on trusted intermediaries, multi-round protocols, or expensive cryptography. We present a sealed-bid auction protocol that executes sensitive bidding logic on a Trusted Execution Environment (TEE)-backed confidential compute blockchain while retaining settlement and enforcement on a public chain. Bidders commit funds to enclave-generated escrow addresses, ensuring confidentiality and binding commitments. After the deadline, any party can trigger resolution: the confidential blockchain determines the winner through verifiable off-chain computation and issues signed settlement transactions for execution on the public chain. Our design provides security, privacy, and scalability without trusted third parties or protocol modifications. We implement it on SUAVE with Ethereum settlement, evaluate its scalability and trust assumptions, and demonstrate deployment with minimal integration on existing infrastructure.
Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek
Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of market inefficiencies, may improve volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, in the present contribution a recently proposed method of disentangling sources of multifractality in time series was applied to the most representative instruments from the digital market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality are temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series.
A mobilidade urbana representa um dos maiores desafios das cidades contemporâneas, sendo a imprevisibilidade do transporte público um fator crítico que impacta milhões de cidadãos e turistas. Atrasos decorrentes de congestionamentos, acidentes e outros eventos inesperados, somados à complexidade das rotas, comprometem significativamente a experiência do usuário. Este artigo apresenta o SIGRÔ (Sistema Inteligente de Gerenciamento de Rotas de Ônibus), uma solução inovadora para o rastreamento e previsão em tempo real da localização de ônibus coletivos. A arquitetura do sistema baseia-se em uma rede descentralizada Web3, na qual cada veículo atua como um nó comunicante em uma malha peer-to-peer (p2p), utilizando GSM LTE-M e, de forma redundante, LoRa, para mitigar falhas de cobertura. Cada ônibus é equipado com sistemas embarcados dotados de Unidades de Processamento Neural (NPUs), que aplicam Inteligência Artificial para corrigir perdas de sinal de GPS e aprimorar estimativas de chegada, integrando dados históricos e em tempo real. O ecossistema é complementado por um aplicativo multiplataforma (iOS, Android, WebApp e sistema embarcado), que oferece planejamento de rotas, visualização em tempo real, informações sobre paradas e uma interface de gestão para operadores, permitindo o reporte de incidentes. O projeto tem como objetivo aprimorar a pontualidade percebida, otimizar a experiência do usuário e fornecer dados estratégicos para a gestão inteligente do transporte público urbano.
Decentralized storage is one of the most natural applications built on blockchains and a central component of the Web3 ecosystem. Yet despite a decade of active development -- from IPFS and Filecoin to more recent entrants -- most of these storage protocols have received limited formal analysis of their incentive properties. Claims of incentive compatibility are sometimes made, but rarely proven. This gap matters: without well-designed incentives, a system may distribute storage but fail to truly decentralize it. We analyze Shelby -- a storage network protocol recently proposed by Aptos Labs and Jump Crypto -- and provide the first formal proof of its incentive properties. Our game-theoretic model shows that while off-chain audits alone collapse to universal shirking, Shelby's combination of peer audits with occasional on-chain verification yields incentive compatibility under natural parameter settings. We also examine coalition behavior and outline a simple modification that strengthens the protocol's collusion-resilience.
El estudio analizó el comportamiento de las criptomonedas Bitcoin y Ethereum durante el año 2024 mediante la construcción de un modelo estadístico ARIMA (AutoRegressive Integrated Moving Average). La investigación utilizó un enfoque cuantitativo que se dividió en cuatro etapas: recopilación y limpieza de datos históricos, verificación de la estabilidad de los datos, identificación y estimación de los mejores parámetros usando criterios de información, y validación mediante medidas de precisión y análisis de residuos. Los resultados demostraron que el modelo ARIMA fue útil en el pronóstico de valores en mercados estables, destacando su trayectoria en el análisis de datos financieros. Además, los valores bajos de RMSE y MAPE validaron que el modelo tiene la capacidad de realizar pronósticos precisos en escenarios con alta frecuencia. En particular, el MAPE de Bitcoin fue 2,25 % y el de Ethereum 2,85 % durante la etapa de prueba, demostrando que los valores pronosticados tuvieron una ligera desviación con respecto a los reales. No obstante, el modelo puede verse afectado en periodos de alta volatilidad, como en las burbujas especulativas o en los desplomes bursátiles, ya que no tiene la capacidad de adaptarse dinámicamente a cambios súbitos en los parámetros; sin embargo, su utilidad puede mejorar al combinar modelos híbridos con ARIMA.
Post-quantum cryptography (PQC) is moving from evaluation to deployment as NIST finalizes standards for ML-KEM, ML-DSA, and SLH-DSA. This survey maps the space from foundations to practice. We first develop a taxonomy across lattice-, code-, hash-, multivariate-, isogeny-, and MPC-in-the-Head families, summarizing security assumptions, cryptanalysis, and standardization status. We then compare performance and communication costs using representative, implementation-grounded measurements, and review hardware acceleration (AVX2, FPGA/ASIC) and implementation security with a focus on side-channel resistance. Building upward, we examine protocol integration (TLS, DNSSEC), PKI and certificate hygiene, and deployment in constrained and high-assurance environments (IoT, cloud, finance, blockchain). We also discuss complementarity with quantum technologies (QKD, QRNGs) and the limits of near-term quantum computing. Throughout, we emphasize crypto-agility, hybrid migration, and evidence-based guidance for operators. We conclude with open problems spanning parameter agility, leakage-resilient implementations, and domain-specific rollout playbooks. This survey aims to be a practical reference for researchers and practitioners planning quantum-safe systems, bridging standards, engineering, and operations.