| Journal of Collective Sciences and Sustainability

Published Online: 30 September 2026

J. Collect. Sci. Sustain., 2026, 2(3), 26413 | Volume 2 Issue 3 (September 2026) | DOI: https://doi.org/10.64189/css.26413

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

Collective Sciences and Sustainability: Integrating AI,

Resource Optimization, and Societal Welfare

Ümit Ağbulut*

Department of Mechanical Engineering, Faculty of Mechanical Engineering, Yıldız Technical University, Yıldız, Barbaros Blv., 34349

Beşiktaş/İstanbul, Türkiye, İstanbul, Türkiye

*Email: eic.css@gr-journals.com (Ümit Ağbulut)

The rapid evolution of sustainable development strategies, collective sciences, and digital infrastructure

continues to reshape the way we manage resources, optimize ecological systems, and build resilient

communities.

[1]

Nowadays, the convergence of these paradigms with artificial intelligence (AI), machine

learning (ML), and advanced engineering is enabling innovative, data-driven solutions across energy footprint

management, environmental engineering, public safety, and social welfare systems.

[2]

As these technologies

advance, interdisciplinary research remains essential for translating computational models and scientific

insights into practical, scalable, and impactful sustainable applications.

[3]

The Journal of Collective Sciences and Sustainability (Volume 2, Issue 3) consists of seven articles from rapidly

growing fields, with research spanning energy-constrained artificial intelligence, smart system security

evaluation, ecological wastewater treatment, predictive water resource analytics, thermal engineering,

centralized web platforms for community welfare, and geospatial crime forecasting.

In this present issue, Balaselvakumar and Santhi provides a systematic review synthesizing 29 evidence units

from 46 publications to address the fragmented landscape of predictive policing and geospatial artificial

intelligence (GeoAI) in India. The authors critically evaluate traditional spatial methods alongside advanced

machine-learning and deep-learning approaches, identifying critical limitations in the existing literature such

as a heavy reliance on retrospective hotspot mapping, a lack of independent replication, and weak reporting of

data provenance. By proposing an integrated evidence framework that separates descriptive spatial evidence

from predictive claims, this study establishes six strategic research priorities, including bias auditing, climate-

crime integration, and cross-state benchmarking, to foster more transparent, reproducible, and context-

sensitive GeoAI systems within public administration.

[4]

Parnika Thakur presents an energy-aware, software-

oriented architecture designed to limit the energy consumption of local Information and Communication

Technology (ICT) infrastructure and prolong the lifetime of legacy enterprise hardware. The system utilizes

request-based model cascades and an emergency load-based admission control module to dynamically manage

model complexity and inference accuracy during periods of high computational workload. Evaluated via trace-

driven numerical experiments on the Alibaba Cluster Trace, the study demonstrates that retaining legacy

enterprise hardware significantly reduces net lifecycle carbon emissions compared to premature replacement

on energy grids with abundant renewables.

[5]

Mukadam et al. present a 3-layer system evaluation framework

designed to address the "local optimization fallacy" in smart city emergency response systems, where narrow

optimization of location precision often degrades overall battery life and alert reliability. Evaluating the

framework through a simulated prototype Smart Tourist Safety Monitoring System across 500 tourist

trajectories and 2,126 synthetic alert events, the study demonstrates that replacing constant GPS polling with

adaptive geofencing reduces simulated GPS energy consumption by 83.5%. Furthermore, by integrating a

temporal SMS fallback and a human-in-the-loop cancellation mechanism, the proposed multilayer optimization

yields a 27.7% overall reduction in false-alarm escalations, confirming that balanced multidimensional design

consistently outperforms single-metric maximization in resource-constrained Internet of Things (IoT)

deployments.

[6]

Afeez Oladeji Amoo evaluates a hybrid Coconut-Shell-Derived Biochar (CSDB) membrane

system coupled with machine learning models to predict and optimize pollutant removal and membrane fouling

in agro-processing mill wastewater treatment. Utilizing a two-stage experimental design, the study trains

multiple algorithms, finding that XGBoost and Artificial Neural Network (ANN) models deliver the highest

predictive accuracy with test-set 𝑅

values ranging from 0.91 to 0.96. Ultimately, multi-objective optimization

determines that a CSDB dosage of 12–15 g/L, a contact time of 90–105 minutes, and a transmembrane pressure

of 1.0–1.1 bar achieve maximum pollutant removal while reducing fouling resistance by 35–45%, offering a

highly cost-effective and sustainable pathway for treating agricultural effluents.

[7]

Twenefour et al. present a

methodological simulation study investigating validation pitfalls, specifically target circularity and optimistic

within-domain validation, that artificially inflate machine learning performance when predicting the Water

Quality Index (WQI). Evaluating XGBoost, Random Forest, linear, and Ridge regression models across a

synthetic 150-record benchmark generated for the Birim, Offin, and Pra sub-basins, the authors demonstrate

that full-feature models drop to negative 𝑅

values upon the removal of index-defining variables. Furthermore,

the models exhibit exceptionally weak geographic transferability, with out-of-basin XGBoost performance

plunging between -0.078 and -22.982, highlighting that strong local predictive capabilities can coexist with poor

regional generalizability regardless of whether linear or tree-based algorithms are deployed.

[8]

Tummalapalli

et al. conducted an experimental and computational investigation into a 204 mm copper–water heat pipe

equipped with a stainless-steel mesh wick to optimize small-scale passive thermal management systems.

Utilizing ANSYS steady-state thermal simulations alongside laboratory testing, the study evaluates temperature

distributions, thermal resistance, and phase-change characteristics, comparing a copper-walled system against

a stainless steel 304 (SS304) equivalent. The copper heat pipe achieved a conduction-based heat-transfer rate

of 31.15 W and a low thermal resistance of 1.605 K/W, whereas the SS304 alternative exhibited a significantly

higher thermal resistance of 42.4 K/W, confirming the structural superiority of copper-water systems for high-

performance, compact cooling applications.

[9]

Shaikh et al. present the design and evaluation of RescueTails, a

full-stack web application built on the MERN (MongoDB, Express.js, React.js, Node.js) technology stack, to

address the fragmented nature of urban animal welfare and stray rescue coordination. The centralized digital

platform streamlines the reporting of distressed animals, coordinates adoption workflows, and enables

structured resource allocation between citizens, NGOs, veterinary services, and foster homes. JMeter-based

benchmark load testing and functional verification demonstrate stable performance and subsecond API latency

under evaluated conditions, showcasing the operational feasibility of scalable social-impact technology in

addressing gaps in urban community infrastructure.

[10]

In conclusion, this issue underscores the vital role that computational optimization, robust validation

frameworks, and centralized digital infrastructures play in advancing global sustainability. By bridging the gap

between theoretical data science and field-specific applications, whether in energy grids, water basins, thermal

management, or urban safety networks these studies demonstrate that sustainable development cannot rely

on isolated technical metrics. Instead, true progress requires a holistic, collective approach that harmonizes

technological innovation with socio-technical resilience and ecological responsibility.

Conflict of Interest

There is no conflict of interest.

Artificial Intelligence (AI) Use Disclosure

The author declare that artificial intelligence (AI)-assisted tools were used only for language refinement,

grammar improvement, and manuscript structuring purposes during the preparation of this work. All content

and interpretations were reviewed and verified by the author.

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