Carbon dioxide (CO2) plays a central role in various chemical and environmental processes, and process intensification is often needed to improve efficiency. Microchannel reactors are well-suited for such applications because they provide enhanced mass transfer, particularly when slug flow is formed due to their high interfacial area. Computational Fluid Dynamics (CFD) is widely used to investigate slug flow formation and gas-liquid interface dynamics. However, despite extensive studies, few verified and validated models are available for accurately predicting slug flow, especially in horizontal circular T-junction microchannels. This study aims to develop, verify, and validate a reliable computational fluid dynamics model to simulate slug flow formation using the volume of fluid (VOF) method. The use of CO2-water system provides realistic hydrodynamic behavior relevant for studying CO2 hydrodynamics inside microchannel reactors. Mesh sensitivity analysis was conducted using seven meshes to ensure mesh independence and computational efficiency. Mesh sizes beyond 370,000 elements showed only minor improvements in prediction accuracy. The model was then validated against experimental data by comparing the bubble length under multiple flowrate conditions, revealing strong agreement with deviations of 3.04%-6.90%. The experimental data showed high reproducibility with an average coefficient of variation of 2.4%, further confirming the model's reliability. This validated model can serve as a foundation for future CO2 studies on hydrodynamic optimization, such as the generation of flow pattern maps in microchannel reactors.
This study investigates NiAl-based composite materials produced by spark plasma sintering with various alloying additives (Cr, Co, Ti, Mo, V, Re, and Zr) and architectures (single-, two-, and three-layer configurations). The materials comprise an FCC solid solution matrix and regions with a mixed FCC-BCC structure containing large NiAl phase B2 intermetallic particles. The materials also contain the TCP phases of the Cr-Mo system (sigma- and & micro;-phases). Electrochemical and corrosion studies were conducted in an aqueous solution (25 g/L CsCl + 11 g/L KCl + 9 g/L NaCl). The multilayer material NiAl-NiCrCoMoVReTiZr/NiCrCo/NiAlCoCrMoReTiZr exhibited the most favorable characteristics in terms of the ratio of current density in the passive state and the extent of the passive state region, namely, 299 mV and 0.0014 mA/cm2. The most extended region of the passive state was found for the single-layer material NiAl-NiCrCoMoVReTiZr. However, its current densities in the passive state were higher (0.0033 mA/cm2) versus 0.0013-0.0015 mA/cm2 for other alloys. The lowest current density in the passive state was obtained for the NiAl-CrMoCoV material. Corrosion tests over a 6-month period revealed no mass changes or pitting traces in the material structure. This may be attributed to the chemical composition of the NiAl-based alloys, the presence of high-entropy regions, and the formation of intermetallic compounds, all of which enhance corrosion resistance.
Environmentally friendly and biodegradable composites are increasingly being studied for use in compatible films, safe food packaging, and wound dressings. Strengthening composite films with modified natural fibers has received limited attention. In this study, biodegradable composite films reinforced with abaca fibers coated with copper nanoparticles were successfully prepared. Copper nanoparticles (CuNPs) were incorporated to provide both antimicrobial and conductive properties. Copper nanoparticle-coated abaca (banana) fibers (CuNPs) were synthesized by the stepwise reduction method, in which the nanoparticles were first prepared and subsequently immobilized onto abaca fibers (AF). This method produced stronger treated fibers than those prepared using a simultaneous reduction method. CuNPs on the surface of PVAcoated AF are nanosized and well-dispersed particles, resulting in higher electrical conductivity compared with the control (PVA-AF only). The incorporation of CuNP-coated PVA-AF into the composite film matrix (containing PVA, tapioca starch, glycerol, and chitosan) significantly improved the tensile strength and elongation compared with the control film. The thickness of films containing higher PVA content (60%-80%) increased compared with those with lower PVA proportions. A similar trend was observed for the water absorption, tensile strength, and elongation properties. The antibacterial activity of CuNP-coated AF showed an inhibition zone of 14 mm, which was comparable to that of gentamicin (17 mm). However, the antibacterial effect was localized and did not diffuse throughout the film.
This study investigates the mechanisms by which leadership style influences FinTech orientation in financial institutions. Grounded in complexity theory and the dynamic capabilities framework, this study explores how strategic agility and innovation function as mediating capabilities linking leadership behavior to FinTech readiness. A structured quantitative survey was administered to top managerial-level employees across banks, insurance firms, and microfinance institutions. A total of 104 complete and valid responses were collected using Go ogle Forms. Structural Equation Modeling using the CB-SEM technique was applied to analyze the data and assess the direct and mediated relationships among the constructs. The study reveals that leadership style has a significant impact on FinTech orientation, both directly and indirectly, through its impact on strategic agility and innovation (total effect leadership style -> FinTech: /3 = 0.457, t = 5.632). While the overall influence of leadership on FinTech orientation was found to be significant, the direct influence was reduced when the mediating factors were controlled for (indirect effects: leadership style -> strategic innovation -> FinTech: /3 = 0.227, t = 2.121, p = 0.034; leadership style -> strategic agility -> FinTech: /3 = 0.420, t = 3.455, p = 0.001). Strategic agility was the strongest predictor, highlighting its critical role in achieving responsiveness and agility in a financially challenged environment with structural barriers (strategic agility -> FinTech: /3 = 0.512, t = 2.961, p = 0.003; compared with strategic innovation -> FinTech: /3 = 0.330, t = 2.064, p = 0.037; and leadership style -> strategic agility: /3 = 0.580, t = 5.262, p G 0.001 vs. leadership style -> strategic innovation: /3 = 0.486, t = 4.549, p G 0.001). This study makes a significant contribution to the literature by empirically testing the capability-mediated model in an economy beset by conflict, supporting the need to enhance leadership practice to develop internal agility and innovation. The study also provides practical insights to financial institutions' professionals on how to overcome structural issues to enable digital transformation through the use of capabilities supported by strong leadership. Beyond finance, the findings demonstrate how leadership-enabled dynamic capabilities can accelerate digital transformation, strengthen organizational resilience, and support innovation management in structurally constrained and uncertain environments.
Indonesia's sugarcane agroindustry plays a crucial role in gross domestic product, yet it faces threats to supply chain sustainability. Empirical studies on selected sugarcane agroindustries are needed to analyse and improve sustainability performance. This study aims to develop a strategy for enhancing the supply chain sustainability performance of the sugarcane agroindustry through performance measurement and empirical case studies. This study employs the fuzzy inference system method, multidimensional scaling, and an adaptive neuro-fuzzy inference system to assess supply chain sustainability performance. This study focuses on economic, social, environmental, and resource sustainability dimensions using 29 indicators. These indicators are subsequently aggregated to evaluate the supply chain's overall sustainability performance. Empirical studies were conducted on two sugarcane agroindustry supply chains to assess the effectiveness of the supply chain and develop strategies for enhancing performance. The sustainability performance of sugar factories by 2023 is almost sustainable and medium sustainable. This study successfully developed lessons learned for sustainability improvement strategies tailored to each agroindustry based on key indicators. Agro-industries are expected to enhance their supply chain sustainability performance and ensure long-term economic, social, and environmental benefits by implementing these strategies. The following lessons were extracted: operational implementation and indicator adjustment, data quality and infrastructure, data pre-processing, and interpretation for managerial decision-making. The methodology for analysing performance and improvement strategies can be implemented to improve the future sustainability performance of the sugarcane agroindustry supply chain.
This study introduces the AI Regional Asymmetry (AIRA) methodology-a comprehensive framework for assessing and mitigating disparities in artificial intelligence (AI) development across countries and regions. Building on economic theories of inequality and resource complementarity, AIRA comprises four interlinked stages: (1) construction of a synthetic AI asymmetry index based on 23 indicators consolidated into four key dimensions - computational chasm, talent gravity, data monopolization, and capital cycle; (2) quantification of asymmetry using economic inequality metrics such as quantile gaps, Gini, and Theil indices; (3) identification of complementary country profiles to form strategic alliances - vertical, horizontal, or multilateral - aimed at resource exchange and imbalance reduction; and (4) scenario modeling to simulate the dynamic impacts of such alliances on global AI market structures. Applied to Belarus as a case study, the methodology reveals potential partnership configurations within the CIS region, with leading economies such as the United States and China, and with developing countries, thereby illustrating opportunities for regional strengthening and global asymmetry reduction. The framework offers policymakers quantitative tools for fostering equitable AI ecosystems, underscoring international cooperation as a strategic pathway to narrow digital divides and promote sustainable, inclusive growth in AI-driven markets.
In recent years, sensor-based human activity recognition has become an active research topic. Sensor data are typically represented as time series, which require a segmentation process before feature extraction and machine learning-based classification. The sliding window is one of the most commonly used segmentation techniques; however, determining the optimal window length remains a major challenge for achieving accurate activity recognition performance. This study proposes a semi-adaptive sliding window method that integrates static and dynamic strategies using accelerometer sensor data. The proposed approach exploits temporal information by considering the current, past, and future windows, each of which is further divided into three sub-windows. The window size is adaptively updated based on the similarity among pairs of subwindows forming a triplet subwindow, and a growth-capping mechanism is incorporated to prevent excessive window expansion. Performance evaluation was conducted using the XGBoost and LightGBM classifiers on the FORTH-TRACE, SBHARPT, WISDM, and PAMA2 datasets. The experimental results show that using XGBoost and LightGBM, the proposed method achieves accuracies of 97.26% and 97.26% on the FORTH-TRACE dataset, 98.09% and 98.15% on the SBHARPT dataset, 98.97% and 99.06% on the WISDM dataset, and 92.21% and 92.43% on the PAMA2 dataset, respectively. These results demonstrate that the proposed semi-adaptive sliding window approach consistently improves human activity recognition performance.
Construction tender evaluation is a high-stakes decision process in which contractor selection is expected to remain transparent and defensible. Although artificial intelligence (AI) effectively enhances analytical decision processing scalability using machine learning, AI adoption in project tender evaluation is constrained by limited interpretability and weak justification of AI insights. This study develops a conceptual Explainable Artificial Intelligence (XAI) tender evaluation model that integrates data preprocessing, predictive modeling, and SHAP explainability within three phases. The model provides decision insights at global and contractor levels through dataset-level feature attribution, contractor-level explanations of evaluation criteria and trade-offs, and project governance insights supporting audit trails and tender award justification. A pilot study was conducted among 10 Malaysian construction sector experts to examine the relevance and practical applicability of the proposed model. The findings indicate XAI strengthens for decision transparency, improves tender ranking interpretability, and supports transparent tender deliberation, whereas professional judgment remains central to a final tender decision award. This study strengthens the link between predictive analytics and procurement governance by explicitly revealing the interaction dynamics of ranking criteria that are often obscured in conventional tender evaluation. This study positions data governance as a prerequisite for credible explanations and decision support. Future research should empirically test the proposed model in live tender evaluation settings and establish sectoral standards for explainability and data governance for construction projects.
Resource allocation in wireless networks is inherently complex, a problem intensified in 5G by heterogeneous traffic classes and stringent quality of service (QoS) requirements. This challenge poses significant difficulties for traditional scheduling methods. In this study, we address these limitations using novel hybrid reinforcement learning (RL) architectures evaluated in a dynamic and realistic network environment. We designed and implemented three hybrid RL algorithms: Asynchronous Advantage Actor-Critic integrated with Proximal Policy Optimization (A3C-PPO), A3C with Proximal Policy Optimization and Session Persistence (A3CPPO-Persistent), and A3C with Twin Delayed Deep Deterministic Policy Gradients (A3C-TD3). These were compared against baseline A3C and Advantage Actor-Critic (A2C) approaches, as well as traditional proportional fair (PF), maximum rate (MR), and Round Robin (RR) schedulers. Simulations were performed in a challenging multicell environment with mobile user equipment and bursty traffic flows across four network traffic types: ultra-low latency (ULL), voice over IP (VoIP), vehicle-to-everything (V2X), and video streaming. Our hybrid RL schedulers showed promising performance in this highly dynamic setting, with A3C-PPO achieving the most balanced overall results, exhibiting 25%-40% lower average jitter and over four times higher packet delivery ratio (PDR) than traditional schedulers under heavy loads. Our results indicate that hybrid RL methods, particularly A3C+PPO, can provide resilient adaptive scheduling that can outperform both conventional techniques and standard RL algorithm models in realistic 5G networks.
Extremely high plasticity soils, such as bentonite, present substantial challenges in geotechnical applications due to their high water retention capacity and expansive behaviour. This study evaluates an integrated physical-chemical stabilization approach using bamboo leaf ash (BLA) to reduce the plasticity of such problematic soils. Bentonite was selected as a sample of soil with extremely high plasticity. BLA, which was made from three types of bamboo and treated through controlled burning, was used as a chemical stabilizer. Mayan bamboo was chosen for soil stabilization owing to its high silica (SiO2) content and pozzolanic reactivity. Numerous geotechnical tests, such as Atterb erg limits and compaction tests, were performed following the ASTM standards. Scanning Electron Microscopy (SEM) combined with Energy Dispersive Spectroscopy (EDS), X-Ray Fluorescence (XRF), and X-Ray Diffraction (XRD) investigations were used to check how the soil changes at a microscopic level, particularly the shape and mineral content after stabilization. The results showed that BLA greatly lowered the plasticity index (PI) from 455.41% to 180% and the liquid limit (LL) from 568.70% to 270%, with only small changes in the plastic limit (PL). The microscopic analysis showed the formation of cement-like materials such as calcium silicate hydrate (C-S-H) and calcium alumino-silicate hydrate (C-A-S-H), which means that the pozzolanic reactions worked well. Using BLA along with compaction provides a sustainable and effective way to reduce the plasticity value of the soil and automatically increase the strength of soils with extremely high plasticity. These results show that BLA could be a green and practical option for soil stabilization using a large number of local plant materials.
A multi-objective neuro-fuzzy control strategy is proposed for a Van de Vusse continuous stirred tank reactor (CSTR), a benchmark system characterized by nonlinear dynamics and non-minimum phase behavior. The controller is based on a multi-input multi-output adaptive neuro-fuzzy inference system (ANFIS) whose parameters are optimized using the NSGA-II algorithm. The proposed framework adjusts membership functions, rule consequents, and integral gains simultaneously within a Pareto-based formulation that considers tracking performance (ITAE) and control effort. The results of the closed-loop simulation indicate improved performance compared to a classically tuned parallel PID controller and a non-optimized ANFIS baseline. The optimized controller reduces the ITAE from 159.17 (PID) and 50.29 (baseline ANFIS) to 2.51, while operating within thermal safety constraints. The controller can compensate for the inverse response dynamics within the simulated conditions. Robustness analysis under +/- 10% parametric uncertainty demonstrates stable performance within the evaluated scenario, although broader uncertainties, such as measurement noise and actuator dynamics, were not considered. Targeted ANOVA provides limited insight into the influence of selected integral gains, identifying the flow-related gain as a relevant factor, but does not constitute a comprehensive statistical validation of the full controller structure. Overall, the proposed ANFIS-NSGA-II framework is presented as a simulation-based proof of concept that shows potential for nonlinear process control. However, further validation under more realistic conditions and experimental implementation is required to assess its practical applicability and generalizability.
The agricultural sector in the Russian Federation generates substantial volumes of organic waste, creating significant environmental challenges and presenting an opportunity for value-added products such as biochar. While biochar offers proven benefits for soil improvement, carbon sequestration, and waste valorization, no systematic, nationwide assessment of its market potential exists. This study aims to develop and apply a structured, multi-criteria approach to evaluate and rank the biochar sales potential of Russian regions. A fuzzy multiple-criteria decision-making model was constructed using six proxy indicators linked to key biochar applications: mineral fertilizer application rate (X1), fresh water usage for irrigation (X2), area of degraded land (X3), feed consumption per conventional head of cattle (X4), cattle population (X5), and sales volume of main agricultural products (X6). Weights were assigned based on each application's scale and quality requirements. The model was applied to statistical data from 78 Russian regions, and K-means and hierarchical clustering analysis were used to validate the results. The primary outcome of this study is the development of a replicable fuzzy multi-criteria approach for assessing the market potential of biochar. The application of the approach is demonstrated through the first comprehensive ranking of Russian regions by biochar sales potential. The Krasnodar Territory was identified as having the highest potential (indicator value = 0.186), followed by the Rostov Oblast (0.320) and the Saratov Oblast (0.335). In total, 15 regions were classified as having "High" or "Very High" potential, whereas more than 20 fell into the "Medium" category. Cluster analysis validated these rankings and revealed three distinct regional profiles based on their underlying socio-agro-economic characteristics. This study provides a novel, data-driven spatial framework for assessing the potential of the biochar market at a national scale. The resulting regional ranking offers a foundational tool for informing strategic decisions on the siting of biochar production facilities and for designing targeted, region-specific market development policies for Russia's emerging biochar economy.
This study investigates graphene oxide (GO) derived from recycled graphite in spent dry cell batteries as a friction modifier in polyalphaolefin (PAO)-based lubricants. GO was incorporated at weight fractions of 1, 3, and 5 wt% to evaluate its influence on friction reduction, wear mitigation, and lubrication film stability. The incorporation of GO significantly enhanced the tribological performance of PAO compared with that of the neat base oil. A nonlinear concentration-dependent relationship was observed between the coefficient of friction (CoF) and wear scar diameter (WSD). The 1 wt% GO-PAO formulation exhibited the lowest CoF, achieving approximately a 45% reduction relative to pure PAO, a 54% decrease in WSD, and a 9% improvement in lubricant film stability. This superior friction performance is attributed to the formation of a thin, well-dispersed tribofilm that effectively reduces interfacial shear stress under boundary lubrication conditions. In contrast, the minimum WSD was obtained at 3 wt% GO, displaying that the formation of a thicker or more compact protective layer enhanced the load-bearing capacity. Increasing the concentration to 5 wt% did not yield further improvement, likely due to reduced dispersion efficiency at higher loading levels. Overall, 1 wt% GO demonstrates optimal friction-reducing behavior, while moderate concentrations primarily contribute to enhanced wear resistance, highlighting a concentration-dependent friction-wear trade-off. These findings demonstrate a viable and sustainable pathway for up cycling battery waste into high-value lubricant additives, contributing to the development of multifunctional and environmentally friendly tribological systems.
This study applies Conservation of Resources (COR) theory to examine technology acceptance in rail-based public transportation through an integrated structural path model linking the Big Five personality traits, intention to use, social support, user experience, and continuance-oriented technology acceptance. Survey data from 584 commuters in the Jakarta metropolitan area, Indonesia, were analyzed using Structural Equation Modeling (SEM). The proposed model shows good fit and substantial explanatory power (R2 = 0.58 for user experience, 0.72 for social support, and 0.79 for technology acceptance). The findings indicate that technology acceptance follows multiple entry points rather than a single uniform route: agreeableness, conscientiousness, and neuroticism are associated with intention to use, openness with social support, and extraversion with user experience. Intention to use also plays a dual role by directly influencing and indirectly shaping technology acceptance through social support and user experience. Overall, this study extends conventional linear acceptance models by offering an integrated and context-sensitive explanation of sustained public rail acceptance in an urban collectivist setting. As this study is based on cross-sectional data, future research could further examine these pathways using longitudinal or experimental designs.
Yb3+/Er3+ co-doped SrLaAlO4(SLA: Yb3+/Er3+) phosphor is a potential upconversion luminescent material with strong green emission for high-power white light-emitting diodes (LEDs). This work used the SLA: Yb3+/Er3+ phosphor for the white LED by blending it with yellow phosphor and SiO2 particles, which is called the SLA: Yb/Er@SiO2 mixture. The SLA: Yb3+/Er3+ phosphor was created with a steady Er3+ ion concentration of 2 mol%, while that of the Yb3+ was adjusted in 1-7 mol%. Under the infrared laser excitation (980 nm), the collected data on luminescence measurement shows that the SLA: Yb3+/Er3+ exhibited both upconversion green and red-color emissions in its luminescence band. Moreover, with 4 mol% of Yb3+, the highest green-emission intensity was observed. A fabricated white LED comprising SLA: Yb/Er@SiO2 compound placed on the blue LED chip was examined with different SiO2 amounts. The obtained data showed an increase in the green luminescence power and lumen output of the white LED with increasing SiO2 concentration. The presence of SLA: Yb/Er@SiO2 helped reduce the color deviation for enhanced color uniformity. Thus, this greenemission SLA: Yb/Er@SiO2 compound can be a competitive material for the development of solid-state lighting.
The highly invasive oriental fruit fly has caused significant agricultural losses worldwide. Electronic traps have been widely studied for fruit fly detection and counting. However, research focusing on applying acoustic sensors to identify fruit flies based on their wingb eat sound is currently lacking. This study focused on identifying trapped oriental fruit flies based on wingb eat sound data. An acoustic sensor was integrated into the funnel trap to record the wingb eat sounds of trapped flies along with ambient environmental noise. The trap was deployed in an apple orchard for two months to collect data. A spectrogram transformation and Mel-filter bank were applied to process the captured audio, generating two distinct sets of spectrogram images. A deep learning model based on convolutional neural network architecture was then designed and deployed on an ESP32 microcontroller to classify the wingb eat sounds of fruit flies and other environmental sounds. The trained model's field experiment in the orchard showed that the model could classify the sound of fruit fly wingb eat in real-time audio streams with an accuracy of up to 96.86%. This demonstrates the practical applicability of the sound-sensorbased fruit-fly identification method. In addition, implementing the deep learning model on a microcontroller results in a compact, low-power, and cost-effective electronic trap. As a result, the compact design and low power consumption make this solution a promising approach for real-time monitoring and early pest detection in agricultural environments. However, its broader applicability requires further validation across more diverse datasets, longer deployment periods, and varying environmental conditions.
This study investigates the eco-friendly synthesis of TiO2 NPs using Melastoma malabathricum fruit extract and lime juice as natural reducing and capping agents. The extract was prepared by macerating the sample in ethanol. TiO2 NPs were synthesized via the sol-gel method with five variations: one control sample using ethanol only and four samples incorporating plant extracts with varying lime juice concentrations. The results confirmed the formation of pure anatase-phase TiO2 with crystallite sizes decreasing from 18.17 to 12.43 nm. Optical analysis revealed bandgap energies of 3.09-3.14 eV, suitable for dye-sensitized solar cell (DSSC) applications. The field-emission scanning electron microscopy (FESEM) images revealed more uniform, smaller particles in the capped samples, as supported by the particle-size distribution data. energy-dispersive X-ray spectroscopy (EDX) confirmed that the elemental composition is close to stoichiometric TiO2. Electrochemical analysis indicated that the sam(power conversion efficiency (PCE) = 3.12%) owing to enhanced charge injection, despite a extract and lime juice exhibited improved charge retention (tau e = 205-274 ms), despite their moderate efficiencies (2.26-2.49%). This study demonstrates the significant potential of tropical performance governed by careful optimization of the composition of natural capping agents.
This fundamental research investigates the proof-of-concept effect of a counterflow Shell-and-Spiral Coil Heat Exchanger (SSCHE) on CO emissions from a B30-fueled single-cylinder 7 HP diesel engine under no-load conditions, without dynamometer loading, and establishes a scientific basis before advancing to prototype development with bypass valve temperature control. CFD simulation using SolidWorks Flow Simulation 2023 predicted fuel outlet temperatures of 55 degrees C, 78 degrees C, and 92 degrees C for engine speeds of 1000, 1250, and 1500 rpm, respectively. The experimental setup on a B30-fueled Jiang FA R175 A diesel engine demonstrated actual fuel outlet temperatures of 40.14 +/- 5.77 degrees C, 56.18 +/- 18.26 degrees C, and 77.34 +/- 7.01 degrees C, with CFD deviations of 27.0%, 28.0%, and 15.9%, respectively. CO emission analysis demonstrated significant reductions: 56.03% at 1000 rpm, 27.98% at 1000 and 1250 rpm, respectively, but showed a 7.79% increase at 1500 rpm. Findings reveal the best CO reduction was observed at fuel outlet temperatures of 40 degrees C-56 degrees C (low to medium rpm) under the no-load conditions tested. The three-point dataset is insufficient to establish a temperature optimum, and future controlled experiments using bypass valve modulation are required to achieve this. Statistical analysis: Cohen's d = 3.12, 95% CI [96.8-114.4] ppm, p < 0.001 at 1000 rpm (very large effect); Cohen's d = 2.10, p < 0.001 at 1250 rpm (large effect); and significant increase in CO at 1500 rpm (d = 0.37, p = 0.003). CFD deviations (15.9%-28.0%) attributed to specification-based boundary conditions and steady-state assumptions; the model is treated as a preliminary design tool throughout the study.
Inner-city settlements are not free of risks such as vacancy and abandonment despite their importance in providing housing for citizens and migrant workers. This paper explores the vacancy issue in informal inner-city settlements and examines it through the street network configuration using syntactic measures such as connectivity, integration, and choice. This research studied 16 kampungs in the inner-city area of Surabaya, which were selected based on a set of criteria. Field observations were conducted to collect vacancy data alongside the Space Syntax Analysis, using DepthmapX to analyze angular connectivity, Normalized Angular Integration (NAIN), and Normalized Angular Choice (NACH) at global and local distances. A regression analysis with SPSS was also conducted to understand the significance of each value to the existence of vacant properties. The result shows that a vacancy is more likely to occur where the street has a lower value of angular connectivity but a higher value of NAIN and NACH, both in global and local distance. However, only angular connectivity and NAIN in the global radius are significant in this occurrence. These findings have practical implications for urban planners, policymakers, and community organizations, highlighting that excessive permeability threatens residents' sense of privacy and safety while accessibility is vital for sustaining the link between kampungs and the broader city.
Climate change communication increasingly requires innovative approaches to enhance public awareness. However, existing XR-based climate communication systems rarely provide rigorous technical performance evaluation of integrated multimo dal environments. This study presents the design and technical performance evaluation of an XR-based immersive system integrating three-wall projection mapping, gesture and voice interaction, and multisensory physical feedback. The system is implemented using a unified pipeline combining Unity, MediaPipe, TouchDesigner, Resolume, and Arduino to enable real-time multimo dal interaction. Experimental evaluations were conducted to assess system responsiveness and interaction reliability, including latency analysis using a 240 FPS high-speed camera, gesture recognition accuracy under varying lighting and distance conditions, and voice recognition accuracy across multiple noise levels. Results show a consistent user-perceived latency of 16.68 ms, gesture accuracy of 90-100% in bright conditions and 90% at 160 cm distance in low-light conditions, and voice recognition accuracy ranging from 100% (30 dB) to 30% (80 dB). These findings demonstrate that the system achieves stable and responsive multimo dal interaction in an XR immersive environment. The study highlights key design considerations and operational constraints, providing a technical foundation for future development of XR-based interactive systems. This study is positioned as an initial technical performance evaluation of a multimo dal XR system.
