Osama Mohamed Irfan

Road network screening decides which segments receive limited safety budgets, so its ranking quality bears on the road-safety targets of the 2030 Agenda for Sustainable Development. Fuzzy rule bases built from crash data are often proposed as readable screening aids but are usually tested on field data whose true risk is unknown. This study benchmarks a data-built, grid-based Mamdani rule base and its zero-order Takagi–Sugeno counterpart against Poisson, negative binomial and interaction regression, gradient boosting and an oracle regression. Six data-generating mechanisms, including a rule-structured and a smooth non-linear one, were crossed with three training sizes (250, 800 and 4000), with 200 replicates each, and every method was scored against the known mean. At 800 records, the Mamdani rule base ranked segments with a Spearman correlation of 0.33 to 0.51 under the log-linear mechanisms, against 0.78 to 0.93 for Poisson regression, and placed 25 to 36% of the riskiest decile in its own top decile, against 46 to 76%. Under the rule-structured surface, it drew level with Poisson regression only at 4000 records. A paired ablation traced most of the gap to noisy cell consequents rather than to the grid itself, and roughly one in five neighbouring rule pairs pointed against a known effect direction. In this prediction step, which excludes the empirical Bayes adjustment, count regression ranked most reliably; a small, cross-validated and audited rule base can serve as an interpretability aid.

Sites for solar-based green hydrogen production are usually selected by combining geographic information systems with multi-criteria decision analysis. In this study, a GIS–analytic hierarchy process (AHP) assessment of Saudi Arabia is extended with continuous value functions, objective and hybrid weights, alternative aggregation methods, stochastic multicriteria acceptability analysis (SMAA), a levelized cost of hydrogen (LCOH) model, and a multi-objective allocation of the national production targets. Technical, economic, and environmental criteria are evaluated nationally and for fourteen candidate hubs, with water security and ecosystem protection treated as primary sustainability criteria. The highest suitability is found along the Red Sea coast, where Yanbu and NEOM–Oxagon are consistently ranked first and second. Its dependence on the weights is quantified by SMAA. The cost of hydrogen is governed mainly by the solar resource and the electrolyser cost, whereas suitability is governed by water access and environmental protection, and the two measures complement each other. Portfolios of hubs are identified by exact multi-objective optimization in which the average cost is lower than that of a rank-based allocation at the same level of suitability. The results provide a basis for the siting and capacity planning of the national hydrogen roadmap.

The construction of oil, gas and petrochemical mega-projects in the Gulf Cooperation Council (GCC) states combines high-hazard simultaneous operations with wet-bulb globe temperatures (WBGTs) that exceed occupational limits well before the statutory midday work ban begins. Existing digital tools advise, warn or monitor. None of them withhold a work permit, and that is the gap this paper addresses. Following a design-science approach, this paper specifies and analyses a digital occupational risk management (D-ORM) framework whose central and novel element is a permit engine gated on the measured or forecast WBGT rather than on clock time, with the gate failing safe when the measurement is unavailable. Saudi Arabia serves as the reference case; Qatar supplies the only existing GCC legal precedent for a measured WBGT trigger and is used here as a reference point, not as a proposed Saudi threshold. Five results follow, all of them analytical. A diurnal WBGT model calibrated to published Saudi field measurements places 68% of the daily heat load above 30 degrees Celsius before the Saudi ban starts, and a Monte Carlo perturbation of the model parameters keeps that share above half in 93% of draws. A heat factor applied to residual-risk scoring reorders the task hierarchy, and the ordering survives a plus-or-minus-one-point perturbation of the input scores in most draws. WBGT gating on a conventional day shift costs 54% of productive crew-hours, while a gated split shift recovers 58% of that loss; the same split shift also protects worker income, which falls to 0.46 of the baseline under a gated day shift but to only 0.73 under the split. ISO 1999 median threshold shift shows that an 85 dB(A) trade never reaches a 10 dB audiometric-priority threshold in a 40-year career, while a 95 dB(A) trade reaches it in 3.2 years, which places audiometric triage in the 90 to 95 dB(A) band. Detecting a lost-time injury frequency shift from 0.62 to 0.30 requires about 68 million work-hours per arm, which is beyond a single package-year and explains why such claims should not be asserted from scenario curves. All models are stated in closed form, the status of every number is declared, and no outcome is presented as observed. The practical implication for contractors and regulators is that heat control is a work-authorisation and shift-design problem before it is a monitoring problem.

Fiber–metal laminates (FMLs) are hybrid structures manufactured using alternating layers of aluminium alloy and epoxy-reinforced E-glass fabric, with the stacking sequence [Al/Glass/Al/Glass/Al], offering high specific strength, fatigue resistance, and damage tolerance. However, structural cutouts generate stress concentrations that can significantly affect the mechanical performance and damage behavior of these hybrid polymer-based laminates. This study develops and validates analytical cohesive zone models (CZMs) for predicting stress fields around circular and elliptical cutouts in FML plates, providing a computationally efficient analysis. Tensile specimens with two thicknesses (1.4 and 1.7 mm) were investigated using two cohesive laws: linear, and exponential softening. Model predictions were assessed against experimental results using stress concentration factors. The exponential cohesive law provided the best overall agreement, with Root Mean Square Error (RMSE) values of 24.87 and 20.99 MPa and R² values of 0.281 and 0.311 for the 1.4 and 1.7 mm laminates, respectively. Experimental stress concentration factors ranged from 1.88–3.70 and 2.04–3.51 for the two thicknesses. Elliptical cutouts aligned with the loading direction produced lower stress concentrations, whereas minor-axis alignment resulted in higher concentrations. Increasing laminate thickness improved prediction accuracy. The proposed analytical CZM provides an efficient approach for evaluating stress concentrations and damage-related behaviour in perforated polymer-based FMLs, with potential application in the design and optimization of lightweight composite structures.

Fused deposition modeling (FDM) of polylactic acid (PLA) is widely used, yet most parameter studies tune one mechanical property at a time and leave the conflicts between properties unresolved. This work treats three responses of FDM PLA together: ultimate tensile strength, flexural strength, and Shore D hardness. A Taguchi L9 orthogonal array varied infill density, raster angle, and layer thickness; signal-to-noise ratios and analysis of variance ranked the factors, linear regression linked the parameters to each response, and the NSGA-II algorithm mapped the trade-off surface between them. Layer thickness proved the leading factor for tensile and flexural strength, while hardness answered mainly to infill density; raster angle stayed weak for every response. The Pareto front showed that low infill favors tensile strength while high infill favors flexural strength and hardness, all at the finest layer setting, and these trends were converted into parameter guidelines for tensile-led, flexure-led, and balanced parts. The statistical limits of the screening design are stated openly, the flexural and hardness campaigns are flagged as provisional, and a replicated, standard-compliant confirmation study is set out as the next step.

Fused deposition modeling (FDM) of polylactic acid (PLA) produces parts whose weak interlayer bonding and low as-printed crystallinity limit their tensile performance. This work used a Taguchi L9 orthogonal array with five replicates per cell (n = 5; N = 45 annealed specimens plus five non-annealed controls) to study how annealing temperature (70, 80, and 90 °C) and holding time (40, 60, and 80 min) change the tensile response of a commercial PLA grade (eSUN PLA+) printed on a desktop FDM machine. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) were used in parallel to measure total crystallinity, and XRD was deconvoluted to estimate the α′/α polymorph fractions; the DSC α′→α exothermic shoulder was used as an independent cross-check. Every annealed condition exceeded the non-annealed baseline ultimate tensile stress (UTS) of 39.75 ± 1.28 MPa. The optimum, 47.00 ± 0.97 MPa at 70 °C/60 min, gave an 18.2% gain. Total crystallinity rose from 8.6% (DSC baseline) to 41.8% (DSC, 90 °C/80 min), with DSC and XRD ranking the conditions consistently. ANOVA confirmed both temperature (30.0% contribution) and time (24.2%) as significant at α = 0.05. The new contribution is a combined strength–crystallinity–polymorph map for desktop FDM-printed PLA: the best-performing specimens are dominated by the disordered α′ form, while the stiffer but weaker high-temperature specimens shift toward α. A partial least squares regression on all 50 specimens supports the polymorph-composition role beyond what total crystallinity alone explains. The practical conclusion is that moderate annealing just above the glass transition gives the best balance of crystal content, polymorph character, and geometric stability for FDM-printed PLA.

Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings.

This study addresses a critical gap in understanding how Lean Manufacturing (LM) practices, particularly people-centered approaches, can enhance operational performance within the unique industrial context of Saudi Arabia’s Vision 2030 economic transformation. The concept of Lean Manufacturing involves a systematic approach and principles aimed at enhancing efficiency, minimizing inefficiencies, and boosting output in manufacturing operations. While LM principles are well-established globally, their application in Gulf Cooperation Council (GCC) economies remains understudied, particularly regarding the central role of workforce engagement in successful implementation. The main objective of the study is to investigate the implications of LM on the productivity of the industry sector. Specifically, this research examines how the integration of people-centered practices with traditional LM constructs (Just-in-Time, Jidoka, Stability and Standardization) influences operational outcomes in Saudi manufacturing firms. A survey was conducted among specific private and public enterprises to collect data, yielding a 55.8% response rate and 67 valuable responses from a pool of 120 contacted companies. The sample encompassed small, medium, and large enterprises across seven manufacturing sectors. SmartPLS 3 and SPSS were used to assess the structural and measurement models. Common method bias was evaluated using Harman’s single-factor test. The findings demonstrate that implementing the recommended LM structural model significantly enhances operational performance. Notably, people integration exhibited the strongest influence on operational performance (β = 0.361), suggesting that human-centered approaches may be particularly salient in the Saudi context. These findings offer practical guidance for manufacturing firms seeking to align lean initiatives with Vision 2030 objectives.

In recent years, the food processing industry in the Gulf Cooperation Council (GCC) has faced increasing pressures to improve operational efficiency while improving its environmental performance. This research examines whether Lean Six Sigma (LSS) methodologies can be used as tools to incorporate sustainability into current operational processes at a date processing facility in Saudi Arabia. In addition to illustrating the ways in which production was improved, this research developed and preliminarily validated a Sustainability Integration Index (SII) framework to measure the contributions of improvement projects to sustainable practices in terms of their impact on the environment, society, and economy. Furthermore, this research examined the role of organizational culture as a moderator of the effectiveness of integrated LSS–sustainability approaches using a Cultural Readiness Assessment Model (CRAM). This research addressed production bottlenecks and aligned production with selected United Nation Sustainable Development Goals (SDGs) using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology. Production bottlenecked in packaging operations resulted in schedule overruns and excessive overtime; therefore, the intervention focused on improving the production process in these areas. There were three distinct improvement streams: demand-based resource leveling, advanced production planning to allow for pull-based flow, and targeted maintenance to raise Overall Equipment Effectiveness (OEE) from 48.2% to 74.6%. Results indicated a 23% increase in daily processing capacity, a 38 min decrease in the average length of time of production closures, and estimated annual cost savings of 940,000 SAR (approximately USD 250,000). The SII framework showed a 21.2% improvement in sustainability scores, with a total composite score improvement from 0.66 to 0.80. Social sustainability had the greatest relative increase (+24.2%). Exploratory correlation analysis found that improvements in cultural maturity and cross-functional collaboration are possible predictors of successful sustainability integration; however, the limitations of the single case study limit the ability to draw causal inferences. The results provide both empirical evidence and possible measurement tools to an under-explored area: the use of LSS in Middle Eastern food processing industries with specific sustainability goals. Validation of the frameworks across different industries will be necessary to establish generalizability.