Bahrain Polytechnic

Purpose This study investigates the impact of green intellectual capital (GIC) on green innovation (GI) and firm performance (FP) in Pakistani manufacturing small and medium-sized enterprises (SMEs). The study aims to analyze the mediating role of organizational learning capability (OLC) between GIC and GI and evaluate the direct and indirect effects of GIC and GI on FP. Design/methodology/approach A quantitative research approach was employed, using partial least squares structural equation modeling to test the proposed relationships. The data were collected from 452 executives across Pakistani manufacturing SMEs in sectors including textile and apparel, food processing, and chemical manufacturing. A structured, self-administered survey was conducted over a three-month period (February to April 2025). Findings The results demonstrate that GIC significantly influences GI and FP, with OLC mediating the relationship between GIC and GI. GI also positively impacts FP, both directly and indirectly through GIC and OLC. Practical implications This study contributes to the resource-based and dynamic capabilities literature by highlighting the role of intangible green resources in driving sustainable innovation and enhancing firm performance. For SMEs, investing in intangible green assets and integrating organizational learning capabilities are crucial for fostering innovation and achieving sustainability. Policymakers and business leaders can leverage these insights to support green innovation and promote long-term sustainable growth. Originality/value This study expands on the RBT and dynamic capabilities theory by examining the role of GIC and OLC in driving GI and sustainable firm performance within Pakistani SMEs, offering a unique contribution to the literature on sustainability in emerging economies.

Adrenal gland cancer, particularly adrenocortical carcinoma (ACC), is an aggressive malignancy arising from the adrenal cortex's secretory cells. ACC is often diagnosed at advanced, metastatic stages, necessitating surgical resection. However, its high recurrence rate and late-stage detection frequently require systemic therapies, which are generally ineffective, leading to poor survival outcomes. Early detection is, therefore, crucial for improving treatment efficacy. Identifying PC-12 adrenal gland carcinoma cells is essential for understanding neuroendocrine tumor progression and developing effective therapeutic strategies. This study presents a novel metamaterial (MTM)-based biosensor for detecting PC-12 carcinoma cells with exceptional sensitivity. The sensor features three resonators fabricated on 0.2-mu m-thick aluminum (Al) layers embedded within a 10-mu m-thick polyethylene terephthalate (PET) substrate. The sensor is highly compact with an overall dimension of just 150 & times; 150 mu m2, making it well-suited for integration into portable diagnostic systems. Operating in the terahertz frequency range (0.5 THz to 5.0 THz), the device achieves remarkable performance, with an absorption rate exceeding 99% across eight operating bands and surpassing 99.9% in two bands. Additionally, it boasts high quality factor (approaching 30) and an exceptional sensitivity of nearly 4000,000 THz/RIU. The sensor's outstanding performance is attributed to meticulous geometric tuning and architectural optimization, significantly enhancing its sensitivity for cancer detection. Numerical validation was conducted using full-wave electromagnetic simulations, including electric and magnetic field distribution analysis, surface current mapping, and scattering parameter evaluation. Comparative assessments against state-of-the-art biosensors demonstrated the proposed sensor's superiority in key performance metrics such as quality factor, figure of merit (FOM), and absorption efficiency. The sensor's efficacy in detecting PC-12 adrenal carcinoma cells was verified by integrating it into a Microwave Imaging (MWI) system. The device successfully distinguished between healthy and cancerous cells by capturing distinct electromagnetic signatures through electric (E) and magnetic (H) field variations. These results underscore the sensor's potential as a highly sensitive, non-invasive diagnostic tool for early-stage detection of adrenal gland cancer and other malignancies.

Modern communication systems strategically leverage multi-spectral bands to enable diverse service scenarios, ensuring seamless connectivity with enhanced reliability and throughput. Millimeter-wave (mmWave) 5G New Radio (NR) is essential to meet the escalating data demands of massive IoT deployments while maintaining stringent quality-of-service (QoS) requirements for real-time emerging applications. In this paper, we present a dual-band endfire phased array antenna for 5G NR IoT applications. A novel radiating aperture array, based on an open-ended substrate-integrated waveguide (SIW) horn antenna, is introduced to enable wide dual-band operation with vertically polarized (VP) and symmetric endfire radiation. The antenna is fed by the open-ended SIW, which functions as a backed cavity loaded with parasitic elements to generate multi-mode operation. To meet 5G system requirements, a 4-element antenna array is simulated, fabricated, and tested. The simulated and measured impedance bandwidth achieves-10 dB dual-band operation of 17% (25.2-29.9 GHz) in the lower band and 12.71% (36.1-41 GHz) in the upper band, covering 5G NR bands n257, n260, and n261, with isolation levels better than 15 dB across the operating bands. The measured realized gain reaches 9.4 dBi in the lower band and 11.25 dBi in the upper band. The array offers beam scanning of +/- 48 degrees at 28 GHz and +/- 36 degrees at 39 GHz, achieving a realized endfire gain exceeding 6.5 dBi and 9 dBi, respectively. These results confirm its feasibility for mass integration in future IoT ecosystems.

This chapter explores the rising importance of sustainable financial management in global firms, emphasizing the integration of environmental, social, and governance (ESG) factors into financial strategy. It highlights the transition from short-term profit focus to long-term value creation by embedding sustainability into decision-making. The discussion covers key frameworks, ESG metrics, and financial tools that enhance transparency and performance evaluation. Drawing on evidence from multinational corporations, the chapter shows that ESG integration can strengthen efficiency, risk management, and competitive positioning, despite potential short-term trade-offs. It also addresses challenges such as greenwashing, regulatory differences, and data limitations, while recognizing the growing role of digital technologies and analytics. The chapter concludes that firms adopting integrated ESG approaches are better positioned to achieve sustainable growth and resilience.

The widespread adoption of end-to-end encryption in 5G networks limits the effectiveness of traditional intrusion detection systems that rely on payload inspection. This challenge is particularly critical for detecting Advanced Persistent Threats (APTs), which employ low-rate, long-duration, and stealthy communication strategies to evade conventional defenses. This study presents a privacy-preserving intrusion detection framework that operates exclusively on flow-level traffic metadata without deep packet inspection. Network packets are aggregated into bidirectional flows, from which temporal, statistical, and directional features are extracted to characterize behavioral patterns. A Transformer-based model with self-attention is employed to capture long-range dependencies across encrypted traffic sequences and identify subtle, temporally dispersed attack indicators. The framework is evaluated on a large-scale 5G-relevant dataset containing over one million flow records and compared against classical machine learning, ensemble, CNN, and LSTM models. Results demonstrate high recall and strong F1-score in distinguishing APT from benign traffic. Attention-based and feature-level explanations further reveal that prolonged communication, irregular timing gaps, and directional asymmetry significantly influence detection decisions. The findings support the practicality of explainable Transformer models for secure and scalable APT detection in encrypted 5G environments.