Search Results (1,165)

Staff scheduling at a logistics sorting center must reconcile time-varying parcel arrivals, intraday service commitments, heterogeneous within-shift capacity, and monthly roster rules. We propose the Three-Stage Integer-Programming Scheduling Framework (TS-ISF). Stage 1 is an hourly-causal, same-day-clearing baseline in which parcel arrivals are exogenous data and backlog, processing, and the resulting staffing requirement are endogenous. Stage 2 independently refines the daily problem by assigning every worker exactly one low-throughput hour through linear worker-subgroup variables and by imposing the morning deadline. Stage 3 converts the resulting daily requirement vector into the minimum monthly workforce size. The aggregate flow formulation does not identify parcel-level order; instead, under homogeneous parcels it admits a FIFO-compatible realization. On the accepted 30-day real-data run, Stage 1 requires 10,791 worker-days, the full Stage 2 model requires 11,859 worker-days, and Stage 3 requires 581 workers. The Stage 2 increase is the joint effect of the low-throughput-hour and deadline restrictions, not a deadline-only effect. Exact HiGHS termination records and matched Stage 3 bounds provide distinct computational and analytical evidence, while a formulation-matched monolithic model confirms the decomposition objective under stated separability conditions. Controlled synthetic instances are used only as internal stress tests. TS-ISF therefore provides a reproducible and auditable basis for case-specific sorting-center workforce planning. Full article

Traditional traffic target detection heavily relies on manual processing. However, the latest advancements in deep learning have significantly enhanced the capabilities of target detection and multi-target tracking. To address these challenges, this paper proposes a perception-tracking-reasoning framework based on traffic rules, which is used for vehicle recognition and driving-state analysis in surveillance videos. This framework integrates enhanced vehicle perception, cross-frame identity association, trajectory-state modeling, and interpretable rule reasoning into a unified processing flow. Finally, experiments show that the main advantage of the proposed model lies in its ability to detect small-sized vehicle targets and improve trajectory stability in complex traffic scenarios. Full article

Background: In-center hemodialysis is the dominant kidney replacement therapy modality in Greece and generates substantial recurring expenditure for the public payer. Home hemodialysis is not currently implemented at national scale, but it may reduce long-term public expenditure if early investment in training capacity, equipment and home support is recovered over time. Objective: To evaluate, from the Greek public-payer perspective, the discounted budget impact and net present value (NPV) of implementing a national home hemodialysis program for 300 patients. Methods: We developed a deterministic investment-appraisal model comparing gradual implementation of home hemodialysis with continued in-center hemodialysis for the same projected cohort over 10 years. The in-center comparator was informed by a 2022 Greek patient-level micro-costing study. Home hemodialysis expenditure was constructed from explicit patient-flow equations, resource quantities, unit costs, capital purchases and hospital-tariff offsets. Annual incremental savings were discounted at 3% in the base case. Alternative discount rates, deterministic one-way sensitivity analyses and program-scale scenarios were examined. Fiscal benefit–cost ratio (BCR) and return on investment (ROI) were also calculated. Results: Undiscounted 10-year public expenditure was EUR 69,681,804 for home hemodialysis and EUR 86,700,267 for continued in-center hemodialysis, yielding savings of EUR 17,018,463. During the first 5 years, the program required EUR 1,724,012 in additional expenditure. At a 3% discount rate, NPV was EUR 12,696,564, the fiscal BCR was 2.89, fiscal ROI was 189.3% and discounted payback occurred during year 6. NPV remained positive at 5% (EUR 10,391,382) and across all tested one-way scenarios (range EUR 706,179 to EUR 24,686,948). Conclusions: The modeled national home hemodialysis program generated a positive 10-year public-payer NPV under the base-case and tested sensitivity assumptions. A positive NPV is not, however, a formal Greek health-system decision rule and does not capture health outcomes, patient and family costs, or equity. The findings support staged pilot implementation and prospective collection of Greek real-world data before wider rollout. Full article

Deep eutectic solvents (DESs) derived from choline chloride (ChCl) have attracted significant interest as environmentally friendly alternatives to traditional solvents. However, their wider industrial application is often limited by high viscosity, which is strongly affected by temperature, water content, and the composition of hydrogen-bond donors (HBD). In this study, a new parametric correlation is presented for estimating the viscosity of aqueous ChCl-based DESs over broad composition and temperature ranges. The model combines the Arrhenius viscosity framework with the Grunberg–Nissan mixing rule, explicitly incorporating the effects of hydration and the HBA:HBD molar ratio through a four-parameter formulation. A comprehensive database of 1228 experimental viscosity measurements from various literature sources for eight chemically diverse aqueous ChCl-based DES systems, including monoethanolamine, ethylene glycol, glycerol, phenol, m-cresol, o-cresol, 1,2-propanediol, and 1,3-propanediol, was used for model development. The proposed correlation accurately reproduces experimental viscosities, achieving coefficients of determination between 0.990 and 1.000, with average absolute relative deviations ranging from 6.68% to 10.56%. Unlike existing models that require extensive, system-specific experimental matrices, this model effectively captures the substantial viscosity reduction due to hydration, the temperature dependence of viscous flow, and the influence of HBA:HBD stoichiometry utilizing only four global parameters per DES family based on molar composition. Full article

Transport Layer Security (TLS) 1.3 strengthens Internet privacy by encrypting protocol metadata increasingly used by network monitoring and intrusion-detection systems, while Encrypted ClientHello (ECH) further reduces visibility into connection establishment. This paper presents a systematic and technically grounded review of encrypted-traffic detection approaches under TLS 1.3 and ECH, with an emphasis on their observable features, analytical formulations, and practical limitations. Unlike previous reviews that primarily classify detection techniques, this study explicitly examines the methodological assumptions and mathematical foundations underlying representative approaches, including Random Forest aggregation, Kullback–Leibler divergence, Discrete Fourier Transform (DFT), and Shannon entropy. The literature is systematically organised into machine learning, statistical/rule-based, and behavioural/flow-level approaches and assessed against feature dependency, interpretability, reproducibility, scalability, deployment feasibility, and resilience to reduced visibility. The review identifies continued dependence on TLS-specific or handshake-derived features and highlights persistent challenges in dataset representativeness, cross-environment generalisation, explainability, and adversarial robustness. In contrast, residual observables—including packet timing, size distributions, directional asymmetry, flow dynamics, frequency-domain characteristics, and burst behaviour—remain potentially useful without inspecting encrypted payloads or concealed protocol fields. The synthesis identifies behavioural–statistical fusion as a promising research direction; however, its effectiveness remains empirically unvalidated as an integrated framework. Future research should therefore prioritise reproducible datasets, cross-environment and adversarial evaluation, and lightweight, interpretable detection mechanisms capable of operating under progressively restricted network visibility. Full article

Lean Construction and digital tools such as Building Information Modeling (BIM), common data environments (CDEs), and mobile applications are widely adopted in construction projects but are often implemented through parallel and disconnected workflows. Consequently, Lean production control continues to rely heavily on manual observations, meetings, and spreadsheets, while increasing volumes of digital project data remain underutilized for operational decision-making. This disconnect limits project teams’ ability to detect waste early, stabilize production flow, and learn systematically from recurring issues. This study develops a conceptual Digital Lean Construction (DLC) framework using a design-oriented methodology comprising three stages: synthesis of gaps in existing BIM–Lean integration research, examination of previously validated digital workflows for BIM Quality Control (QC), Quantity Takeoff (QTO), and digital twin monitoring, and integration of these components into a unified closed-loop architecture. The resulting framework organizes project information through four conceptual layers and six implementation components that connect BIM/Industry Foundation Classes (IFC4 × 3) models, schedules, issue and quality records, quantity data, field inputs, sensor information, and GIS-based spatial context. The framework assumes IFC4 × 3 because it provides enhanced support for infrastructure assets and linear referencing required for transportation and civil infrastructure projects. Rule-based analytical logic is formalized for seven Lean key performance indicators (KPIs): Percent Plan Complete (PPC), takt deviations, constraint age, rework cycles, waste event counts, QC status, and delay risk. The framework demonstrates how validated and location-aware project information can be transformed into actionable Lean performance intelligence and incorporated into weekly planning, daily huddles, problem-solving, and standardization routines. Several underlying data-generation components have been validated in previous studies; however, the integrated DLC framework itself remains conceptual and requires project-level empirical evaluation. As a conceptual framework grounded in prior literature and previously validated digital workflows, this study does not include empirical field validation. Instead, it proposes an operational architecture intended to guide future implementation and evaluation in real construction projects. The study contributes an implementable architectural foundation for moving from fragmented, retrospective reporting toward proactive, data-supported, and continuously improving production control. Full article

Anomaly detection in vehicular networks focuses on cybersecurity, leaving physical traffic-flow anomalies at urban intersections underserved by IoT sensing. This paper benchmarks anomaly detection on five days of hourly vehicle counts from four consecutive signalized intersections in Kuwait City, with a vehicle-to-infrastructure (V2I) latency feasibility analysis. Anomalies are synthetically injected because verified incident labels are unavailable; scores reflect detectability under the injection protocol rather than validated incident detection. Across ten injection seeds, CUSUM is the most accurate (mean F1 0.945, perfect precision on every seed), Isolation Forest attains the highest recall (0.955), and the LSTM-AE reaches F1 0.347; on misaligned anomaly classes, the margin narrows, and the LSTM-AE matches CUSUM on gradual drift. A corridor rule localizes detected corridor anomalies (9/9, conditional on detection). Hourly aggregation alone imposes an expected 1800 s detection delay, over 130 times the 13.5 s V2I budget at 80 km/h. A sub-second Tier-1 edge detector, evaluated in traffic-calibrated simulation, detects surges within budget (median 6.8 to 9.1 s, robust to signal-cycle platooning), whereas flow-cutoff detection requires roughly 21 s and overnight hours remain a blind spot. Results support a two-tier edge-cloud design and provide, to our knowledge, the first such benchmark on real Gulf-region corridor count data. Full article

Background/Objectives: Adeno-associated virus (AAV) capsids undergo pH-dependent conformational gating at the 5-fold symmetry pore, but how these structural dynamics shape serotype-specific behavior and affinity-ligand recognition remains unclear, particularly for the clinically important serotypes AAV8 and AAV9. This study aimed to establish a pH-resolved structural framework linking 5-fold pore dynamics to peptide-ligand recognition and to translate this framework into sequence-based design principles for affinity capture of gene therapy vectors. Methods: AAV8 and AAV9 5-fold capsid assemblies were subjected to 500 ns molecular dynamics simulations under acidic (pH 5), neutral (pH 7), and basic (pH 9) conditions, with analysis of pore volume, inter-residue contact networks, electrostatic potential, and solvent-accessible surface area. In parallel, affinity chromatography using three mixed-mode peptide ligands (RVVAVYRI, TTFRAHHI, and TYHHHHII) was performed on clarified HEK293 lysates containing AAV8 or AAV9, with capsid yield, host-cell-protein clearance, and transduction activity assessed by ELISA, SEC-HPLC, and flow-cytometry-based transduction assays. Results: AAV8 displayed a heterogeneous, bimodal pore conformational landscape at pH 7, whereas AAV9 exhibited a discrete gate-like transition with maximal pore constriction at physiological pH; both serotypes showed pore-proximal contact remodeling with distinct network topologies. Experimentally, TYHHHHII achieved the highest selectivity for genome-containing capsids at pH 7, with transduction activity enrichment factors of 2.82 (AAV8) and 5.61 (AAV9), while TTFRAHHI provided the broadest operational pH range for bulk capsid recovery. Conclusions: These findings establish a structural framework linking pH-dependent pore dynamics to affinity ligand recognition and suggest practical sequence-design rules for ligand engineering: clustered histidines for neutral-pH selectivity, Arg-containing motifs for broad-pH robustness, and aromatic or hydrophobic residues for reinforcement of capsid binding. Full article

Robert Shiller proposed perpetual futures in 1993 to create derivative markets for assets that are illiquid or whose price cannot be observed directly. Cryptocurrency markets later built the instrument under a different funding rule. We give a single no-arbitrage result that nests both designs: the perpetual price is the present value of a benchmark flow discounted at the funding rate, so the funding rule fixes both the benchmark and the discount. A random time change represents the price as the expected spot at the first event of a clock whose intensity is the funding rate. This yields the main structural result, that stochastic volatility moves the basis only through the carry, so a volatility risk premium, and not volatility itself, can break the peg. We then read price discovery as nonlinear filtering in which the funding rule is a feedback observer whose gain is the funding intensity and the peg the fixed point of a stochastic approximation, and we give a segmented market equilibrium under which the pre-listing premium is structural rather than behavioral. In the June 2026 SpaceX market, the last pre-listing closes were $172.84 on Hyperliquid and $170.82 on Binance, compared with the listed equity’s $185 close on 18 June and the $135 bookbuilt offer. Simulation matches the pricing results to their closed forms. Generative Bayesian computation recovers the funding intensity sharply but not the softness of the anchor. Full article

In vitro neurovascular unit (NVU) models are essential for reproducing blood–brain barrier (BBB) transport and neurovascular cell interactions. However, the literature remains fragmented: biomaterial chemistry, fabrication parameters and organ-on-a-chip architecture are commonly evaluated in isolation, while inconsistent reporting of matrix properties, processing history, cell source, flow and barrier readouts prevents head-to-head comparison and the extraction of transferable design rules. To address this gap, this review integrates biomaterials, manufacturing technologies and organ-on-a-chip engineering within a unified material–process–structure–function framework. We translate endothelial junctions, basement-membrane components and perivascular cells into experimentally actionable material requirements; compare natural, synthetic, semisynthetic and decellularized extracellular-matrix hydrogels; and examine crosslinking, peptide functionalization, stimuli responsiveness, composite-network formation and preparation methods. Findings from Transwell, microfluidic, tubular, self-assembled and 3D-bioprinted BBB systems are used to relate matrix stiffness, degradability, ligand density, permeability, device-body material and fabrication route to barrier maturation, analytical access and reproducibility. By defining matched controls and minimum reporting requirements for chemistry, mechanics, transport and processing, this review provides a practical basis for next-generation BBB models that can improve permeability and efficacy screening in drug discovery, reproduce disease- and patient-specific barrier dysfunction, and support individualized response testing with iPSC- or patient-derived cells. Full article

Engineering schematics, such as electrical, mechanical, piping and instrumentation diagrams, record how industrial plants are built and operated, but most of them survive only as images or scanned sheets that software cannot read. Digitizing them by hand is slow and error-prone: an expert must find and classify hundreds of symbols, read dense technical text, and work out which label belongs to which component. Progress with learning-based methods has been held back on two fronts at once. There are almost no annotations that connect a text label to its symbol, and the drawings themselves are usually confidential, so even unlabeled sheets rarely reach the public domain. We address this with a system that turns a drawing into a structured, queryable graph: it detects and classifies the graphical components with an object detector, recovers the technical text, and then resolves which label belongs to which component. Our contributions are threefold: (i) the first at-scale dataset of manually annotated text-to-symbol links for industrial schematics; (ii) a complete, deployable digitization system combining tiled detection with sliced inference, off-the-shelf OCR, and a text-to-symbol association stage; and (iii) a rigorous, leakage-free benchmark of association methods. Under an observable-only candidate protocol, we find that on logic circuits association is dominated by geometry: a simple pairwise model reaches about 99% top-1 and a graph neural network matches but does not exceed it, whereas the denser P&IDs still benefit from a geometric rule-based chain. Detection reaches an mAP@50 of 0.995 on logic circuits and about 0.91 across the 107-class P&ID taxonomy. The system produces a partial semantic graph; connecting lines and flow direction are not extracted. Full article

Chlorogenic acid (CGA) exhibits anticancer activity in colorectal cancer (CRC), but its clinical application is limited by low lipophilicity. To improve its physicochemical properties, four CGA esters (methyl, ethyl, n-propyl, and n-butyl chlorogenates) were synthesized and evaluated. Physicochemical properties were characterized in silico, and their biological activity was assessed in SW480, HT-29, and non-tumoral NCM460 cell lines using viability assays and flow cytometry. Molecular docking studies were performed to investigate the interactions of CGA and its esters with proteins involved in cell-proliferation-related signaling pathways. In silico analysis showed a progressive increase in LogP values across ester derivatives. All esters complied with Lipinski’s rule of five, whereas none met Veber’s rule due to their predicted topological polar surface area (TPSA) values. The esters induced dose- and time-dependent reductions in cell viability, with n-butyl chlorogenate exhibiting the strongest cytotoxic activity and a significantly lower IC₅₀ value within the tested concentration range. This derivative showed preferential cytotoxic activity toward SW480 cells while exhibiting only limited effects in non-tumoral NCM460 cells. In addition, n-butyl chlorogenate induced changes in mitochondrial oxidative status and phosphatidylserine externalization, consistent with apoptosis-associated cellular changes. Overall, these findings demonstrate that esterification modifies the physicochemical profile of CGA ester derivatives and is associated with enhanced cytotoxic activity. Increased lipophilicity was associated with enhanced cytotoxic activity, supporting further optimization of these compounds for CRC research. Full article

Concurrent high river water temperature (RWT), low dissolved oxygen (DO), and low river flow (RF) can degrade water quality and aquatic habitat, but their joint probability is rarely quantified in a fully trivariate framework. This study develops a semiparametric trivariate drawable-vine (D-vine) copula model that uses Gaussian kernel density estimation (GKDE) margins with parametric pair-copulas to estimate compound thermal–oxygen–low-flow hazards. The framework provides conditional exceedance probabilities and AND- and OR-joint return periods (RPs) for monthly synchronized RWT–RF–DO states. The analysis uses 117 synchronized monthly triplets from the Tungabhadra and 152 from the Yamuna. Kendall’s $\tau$ values for RWT–RF and RF–DO are $-0.12$ and $+0.12$, respectively, at the Tungabhadra, compared with $+0.24$ and $-0.24$ at the Yamuna, indicating contrasting basin-specific dependence pathways. Candidate D-vine orderings are evaluated through permutation-based centred-variable selection and compared with a minimum-spanning-tree heuristic. The final selection uses information criteria, scoring rules, and calibration diagnostics. The selected structure is RF-centred for the Tungabhadra and DO-centred for the Yamuna. Bootstrap analysis supports the stability of the principal dependence structure but shows higher uncertainty in some conditional tail components. For an RWT threshold of $30°\mathrm{C}$, conditioned on DO and RF below their fifth percentiles, the fitted exceedance probability is approximately 0.90 at the Tungabhadra and 0.99 at the Yamuna. Within the available records, the Yamuna shows higher fitted co-occurrence probabilities and generally shorter trivariate AND-joint recurrence intervals than the Tungabhadra. The findings support risk-based screening of warm, low-flow, oxygen-stressed periods while emphasizing the need for local recalibration of margins, dependence structures, and management thresholds. Full article

Closed-form Caputo gradients in critical-state stress-fractional plasticity have relied on the polynomial Modified Cam-Clay surface. We extend them to the non-polynomial teardrop bounding surface by defining the operator as a Caputo derivative in the logarithm of normalized pressure, orientation-corrected for that decreasing map. On this axis, the surface becomes power–exponential, its critical-state terminal falls at exactly t* = 1/Ψ independently of Ω, and the fractional gradient reduces to incomplete-Beta–Kummer and Humbert-Φ₁ closed forms, verified against singularity-aware quadrature over 1240 cases to relative errors below 10⁻¹¹. The flow rule recovers associated flow as α → 1 and is exactly associated at the critical state. This is a computational study of operator semantics on inherited calibrations, not an experimental validation. Substituting it for the fixed-window Grünwald–Letnikov flow of a companion factorial collapses the dominant flow main effect from −60% to below 0.2% for both clays at every overconsolidation ratio tested, across drained and approximately undrained paths. The collapse is conditional: it is a property of the critical-state dwell, and at a laboratory-scale budget the operators still differ by about 15% of baseline. Window semantics, not fractionality alone, decide where flow influence resides in a factorial design. Full article

Escalating energy costs and peak power demand charges pose significant challenges to the manufacturing sector. In response, industries are increasingly adopting on-site renewable energy sources and Battery Energy Storage Systems (BESSs). However, maximizing their economic benefit requires sophisticated control strategies that integrate energy management with production operations. This paper proposes and evaluates an integrated and adaptive rule-based coordination framework for BESS and machine-level switch-off policies in a production environment. Using discrete-event simulation, we model a four-machine manufacturing flow line powered by the grid and an on-site solar PV plant. We compare six distinct control policies, ranging from a benchmark case without storage to progressively more integrated context-aware strategies that incorporate price-aware BESS charging, dynamic peak-shaving, and adaptive machine switch-offs. The results demonstrate that integrated policies yield substantial economic benefits. The most advanced policy dynamically coordinates BESS dispatch with machine-level switch-off decisions based on electricity prices, production conditions, and energy availability, achieving the largest reduction in total energy costs and peak grid demand among the evaluated policies. This study quantifies the synergistic effects of combining supply-side (BESS) and demand-side (switch-off) strategies, providing a framework for developing resilient and cost-effective energy management systems in modern manufacturing. Full article