Equitable access to opportunities is a cornerstone of social inclusion, and public transport plays a central role in delivering it for the millions of households without access to a private car. Yet most accessibility research has focused on case studies of individual cities, while transport funding decisions in the UK are typically made at regional or national scales. This paper evaluates the infrastructure-based accessibility of bus networks in England, together with their connectivity to key destinations such as places of employment, healthcare and education. We propose a scalable, isochrone-based methodology that compares the area reachable by bus to the area reachable by car within a fixed travel-time budget, and combines it with a destination-count ratio that quantifies the share of destinations reachable by bus relative to those reachable by car. Applied across all 33,755 Lower-layer Super Output Areas (LSOAs) in England, the analysis reveals deep and spatially structured inequalities in mobility-mediated access to opportunities. We show that 61.4% of LSOAs, home to 60.9% of the resident population, reach less than 1% of the equivalent car-accessible area by bus, and that 71% of LSOAs (70.6% of the resident population) reach fewer than 1 in 20 of the destinations a car user could reach. Spatial autocorrelation analysis reveals widespread clustering of low accessibility outside major cities, while urban areas show finer-grained, heterogeneous patterns of disadvantage. The findings highlight rural-urban inequalities and within-city heterogeneity, and point to areas where targeted bus-network investment could yield significant gains in social inclusion. The methodology relies only on openly available timetable and street-network data and is therefore transferable to other countries and applicable at finer geographical scales.
The aim of this endeavor was to create an Artificial Intelligence (AI) based legal researcher that utilizes a multi-stage Retrieval-Augmented Generation (RAG) methodology to solve the challenges of finding and comprehending pertinent legal information in the documents. The traditional methods for finding legal documents will usually yield very few results that are useful to someone without a law degree. This new system uses semantic search, keyword retrieval and Large Language Model (LLM) reasoning methods to improve the legal research process. The submitted query will be transformed into a vector embedding while simultaneously being classified into the appropriate category for retrieval. Once the query is classified, the system will use a hybrid retrieval mechanism to find the most relevant legal document based on both BM25 (Best Matching 25) and FAISS (Facebook AI Similarity Search) search algorithms and will combine these results using Reciprocal Rank Fusion (RRF) in order to determine the best results. Later, it will be processed through LLM models to produce formatted (structured) answers to the query in accordance with the relevant legal citation. The overall performance of the proposed system demonstrates improved answer relevancy, reliability and contextual accuracy, thereby making it an effective tool for legal research and assistance. LLaMA-3.3-70B achieved the highest scores in Answer Relevancy (0.91) and Faithfulness (0.92), proving its ability to generate accurate, intent aligned, and evidence-grounded legal responses with minimal hallucination. Gemini-2.0-Flash showed higher Context Recall (0.78), indicating strong retrieval capability, while Mistral-Large excelled in BLEU (0.769) and semantic similarity, highlighting fluent text generation but weaker evidence grounding.
Data-driven organizations increasingly depend on large, heterogeneous, and fast-moving data to sustain the quality of their products, services, and processes. The transition toward data-driven organizational quality, often discussed under the label of Quality 4.0, has so far been examined mainly through a managerial lens. As a result, the data foundations of that transition remain fragmented and under-theorized. This Conceptual Analysis reframes quality-oriented big data as a first-class data management problem and proposes a layered conceptual architecture that organizes the lifecycle of such data across five interconnected layers: multi-source acquisition, integration and fusion, data quality assurance, analytics and knowledge extraction, and data governance and ethics. The central argument is that data quality operates as a central mediating construct linking big data capability to organizational quality outcomes: analytics improves organizational quality in large part to the extent that the underlying data are accurate, complete, consistent, timely, and well governed. Drawing on the data management, data quality, and machine learning systems literatures, the article specifies the constituent elements of each layer, the dependencies among them, and the failure modes that arise when the data foundation is neglected. Five propositions and a structured research agenda position the acquisition, fusion, quality assurance, and governance of quality-oriented big data as priorities for data mining and management research. The architecture offers a data-centric vocabulary for designing and evaluating the data infrastructures on which data-driven organizational quality ultimately depends.
Recommendation quality in open-network commerce is conventionally measured through offline ranking metrics that implicitly assume reliable upstream retrieval, stable item identities, and consistent catalog availability. In open networks such as India's Open Network for Digital Commerce (ONDC), these assumptions are routinely violated: catalogs are contributed by heterogeneous, independently operated providers, transactions follow asynchronous Beckn-style protocol lifecycles, and identifiers drift across service boundaries. This paper presents LocalMarket, a reliability-aware recommendation platform (Next.js frontend, FastAPI backend, ensemble ranking layer) that implements ONDC-pattern transaction flows in a controlled, repository-reproducible environment. We propose and validate a dual-metric evaluation framework that decouples model effectiveness—measured through Mean Average Precision (MAP@10), Recall@10 and Normalized Discounted Cumulative Gain (NDCG@10)—from serving correctness, measured through a novel Route Resolution Success Rate (RRSR) and Wishlist Consistency Rate (WCR). Using a controlled within-system ablation across Model-only, Reliability-only, and Combined configurations on fixed traffic traces, we utilize a convex-combination ensemble containing neural collaborative filtering (NCF) (utilizing only GMF embeddings for retrieval latency), graph-based, session-aware, and content-based components primarily as a vehicle for testing this framework. While this ensemble configuration improves NDCG@10 by 70.7% over a Popularity-based baseline, our primary finding validates that four targeted reliability interventions raise RRSR from 60.0 to 99.8% and WCR from 0.0 to 100.0%. Critically, the combined configuration preserves both gains simultaneously, indicating that model-level and serving-level improvements are additive rather than substitutive. The results support a systems-level thesis: in open-network commerce recommenders, ranking accuracy and serving correctness are independent, jointly necessary axes of quality, and reporting only one materially overstates the value delivered to end users. We further contribute a reusable integration failure-mode taxonomy, a constrained ensemble-weight selection protocol, and a reproducible experiment-replay procedure intended to generalize to other open-network commerce deployments.
