Based on job postings from the "51job" platform in March 2025, this study employs content analysis to conduct a assessment of 866 valid positions in the industrial robotics industry across nine mainland cities in the Guangdong-Hong Kong-Macao Greater Bay Area. The aim is to examine the structural characteristics and inherent contradictions of talent demand in this industry. The findings are as follows: First, talent demand exhibits significant geographical agglomeration and functional division, with Shenzhen, Guangzhou, and Dongguan forming a synergistic chain of "R&Dtransformation-application." Second, private small and medium-sized enterprises constitute the primary source of labor demand (accounting for over 80%); however, their recruitment strategies demonstrate a strong preference for "immediate combat effectiveness," overly relying on work experience as a signal. This leads to an "experience paradox" and creates employment barriers for recent graduates. Third, the educational requirement structure for talent is pyramid-shaped, with over half of the positions demanding associate degrees (53.89%), while the demand for high-end R&D talent with master's degrees or higher is less than 2%, indicating that the industry currently focuses primarily on integration and application. Fourth, salaries are positively correlated with work experience, establishing a high entry barrier for early-career professionals.

: Since artificial intelligence (AI) was developed, the healthcare sector is changing dramatically. With the promise of more precise and individualized medicine, this age promises to transform patient care, diagnosis, and treatment. Since artificial intelligence (AI) was developed, the healthcare sector. These algorithms can also help with precision medical techniques, early illness identification, and therapy suggestions. This article seeks to address the advantages and disadvantages of incorporating AI while providing a comprehensive analysis of its impacts across multiple domains. Artificial Intelligence-powered robots improve care delivery and automates chores, especially in surgery and rehabilitation. AI is revolutionizing healthcare through patient monitoring, treatment customisation, diagnostic support, operational efficiency, and public health. Forecasting, processing of natural language, and machine learning are just some examples of these technologies.AI systems are highly accurate in diagnosing diseases from medical imagery, and they make it possible to create individualized treatment programs based on patient data analysis.

Industrial machines generate heat during operation, and excessive temperature can lead to equipment failure, reduced efficiency, and safety hazards. This paper presents the design and implementation of an Industrial Machine Overheat Detection and Alarm System using an Arduino Uno, a DHT22 temperature sensor, and a passive buzzer. The system continuously monitors the machine’s temperature and triggers an alert when a critical threshold is exceeded. The Arduino Uno processes real-time temperature data from the DHT22 sensor and activates the passive buzzer as an alarm when overheating is detected. This simple yet effective system enhances industrial safety, reduces maintenance costs, and minimizes downtime due to overheating issues.

This study explores the critical role of engineering management in advancing the infrastructure of maritime museums, with a focus on the coastal city of Sabratha, Libya. Despite its rich maritime heritage and environmental assets, Sabratha currently lacks a dedicated marine science museum. By employing a descriptiveanalytical methodology supported by a local case study, this paper illustrates how engineering management tools and principles can transform the concept of a maritime museum into a feasible project that enhances sustainable development, environmental education, and cultural tourism. The study further presents a preliminary integrated museum design, including a 3D spatial model, grounded in contemporary engineering management practices to address local challenges and opportunities.

Thermal management design for battery used in automobiles are very important in order to manage the thermal dissipated by operating components. This research aims at performing design optimization of a thermal management system for battery modules in modern vehicles. An active cooling systems is proposed where oil cooling system is used for battery modules. The causes of problem due to improper thermal management of battery is deployed to identify the potential failure modes and causes so that improvements can be made for battery modules. The design for the thermal management system were done with SOLIDWORKS. To maintain the temperature of battery helical passage and exterior flow passage is used, the radiator coolant oil is used as the fluid to conduct heat from battery during its passage time. The thermal management system for battery modules is simulated using ANSYS CFD software. Results from simulation were validated and have shown good agreement based on the data collected at various vehicle speeds.

: Steel plate-encased reinforced concrete (SPC) columns are widely employed in civil engineering for their enhanced strength, ductility, and durability, particularly in high-rise and seismic-prone structures. However, defects in steel encasements—such as weld discontinuities, voids, surface cracks, or lamination— can compromise reliability and service life. This paper presents an integrated study combining Non-Destructive Testing (NDT) with Finite Element Method (FEM) analysis to evaluate defect detection and structural performance of SPC columns. Ultrasonic, radiographic, and magnetic particle inspections were performed to identify and classify flaws, while FEM simulations in ABAQUS were used to analyze stress distribution, load– displacement response, and failure mechanisms for both defect-free and defect-affected columns. Comparative results demonstrate that defects significantly influence ultimate load capacity and ductility, with NDT-informed FEM models yielding more accurate predictions than idealized simulations. The proposed NDT–FEM framework provides a reliable basis for developing defect acceptance criteria, retrofitting strategies, and performance-based design guidelines, thereby improving safety, durability, and maintenance planning in steel– concrete composite structures.

The growing demand for sustainable infrastructure and intelligent transportation systems has led to the development of smart highways powered by renewable energy. This paper proposes a Smart Highway System that integrates green energy sources with automated monitoring and control mechanisms to enhance safety, efficiency, and sustainability. The system employs solar panels and wind turbines with Maximum Power Point Tracking (MPPT) to ensure efficient energy harvesting and storage in a 12V battery. An ATmega8 microcontroller serves as the core controller, interfacing with sensors such as an LDR for streetlight automation, a soil moisture sensor for environmental monitoring, and manual switches for user inputs. The processed data is utilized to control various highway applications through a relay module, including intelligent street lighting, water pumping, electric vehicle (EV) charging, and emergency alert systems using GSM communication. Additionally, red LEDs and buzzers provide immediate warnings for hazardous conditions, thereby improving highway safety. The proposed system minimizes dependency on conventional grid electricity, reduces operational costs, and contributes to environmental sustainability by leveraging renewable resources. This model demonstrates an effective approach to integrating smart technology with green energy for next-generation highway infrastructure.

Globally, researchers are looking for low-cost and good-performance solutions to prevent and reduce corrosion. Over the years traditional (chemical) corrosion inhibitors have been used to prevent corrosion. Although these inhibitors have good efficiency, their use has been limited due to high cost, toxicity, non-biodegradability, and health hazards. This study, evaluated the inhibitive properties of nanoparticles obtained from the barks of Ficusthonningii (FT) blended with Hyptis Suaveolens L Poit (HSLP) extract as corrosion inhibitor on mild steel. Leave extracts of HSLP and nano particles prepared from the stem barks of FT were used as corrosion inhibitor for mild steel in 1m H2SO4 using weight loss technique. The study was conducted at room and elevated temperatures of 313, 323, 333 and 343K. The test period was varied from 48 to 336 hours at 48 hours interval, at room temperature and 3 hours at each elevated temperature.The experiments were performed with extract concentrations of 0.1, 0.2, 0.3, 0.4 and 0.5 g/L. with 0.5 g of nano particles from barks of FT blended with HSLP in each concentration. Weight loss measurements were used to compute the corrosion rate and inhibition efficiencies in the various concentrations. The results showed that the corrosion rates increased with time of immersion in the absence of inhibitor but decreased with inhibitor concentration and in the blends of the HSLP/FT nano particles at room and elevated temperatures, while inhibition efficiency increased with increase in inhibitor concentration and in the blends of the HSLP/FT nano particles at room temperature but decreased with temperature rise. Inhibition efficiencies of 93.54 and 96.01% at 0.5 g/L were obtained for HSLP and HSLP/FT respectively at room temperature. At elevated temperatures of 313K to 343K inhibition efficiencies reduced to a minimum of 70% at 0.5 g/L and temperature of 343K implying desorption of inhibitor at higher temperatures. Based on the results obtained, the study concluded that, the extract and blends with nano particles of FT acted as good inhibitors for mild steel corrosion in acidic medium with inhibition efficiencies above 70%. hence, these inhibitors can be used for corrosion control and prevention.

With the increasing complexity and volume of cyber attacks, traditional rule-based systems are often inadequate in detecting novel threats. Machine Learning (ML) techniques have emerged as a potent solution for identifying anomalies in cyber security logs, offering the ability to learn from data and detect suspicious patterns with minimal human intervention. This This review paper presents a comprehensive survey of existing ML approaches for anomaly detection in cyber security logs. We break down well-known datasets, estimate performance criteria, bandy important difficulties and undiscovered investigation directions, and classify styles into supervised, unsupervised, and semi-supervised orders.

: In this study forced vibration of Chery 100hp tractor transfer case was analyzed using mathematical modeling. The system defined as a forced vibration damped and with a single degree of freedom, all the important feature in the system has been represented, mathematical and analytical equation has been derived including governing equation by using D’Alemert’s principle which is the principle of conservation energy. The system was analyzed based on damping oil type by varying damping ratio (ξ) between 0 to 1 between under damped to over damped to analyze resonance situation, mechanical material property and force excitation by talking 95 hp and 100hp reference. By importing the governing equation in to Mat Lab software amplitude vs. time graph was plotted by taking ξ=0 with 100 hp and 95 hp, ξ=1 with 100hp and 95 hp with various material type. Its observed that the system is not disturbed on the available oil damper (SAE 140) when the excitation force is 95 hp but there will be a problem on 100hp and the same oil type. That implies one of the failure reason for the system is damping oil type. Taking the best transfer case oil specification which are For SAE J306 is 5.29*10^(-6), SAE 90 is 0.027, SAE 140 is 5.3*10^(-6), SAE 250 is 4.81*10^-6, ISO 100 is7.44*10^-6, ISO220 , 5.68*10^-11, ISO 150 is 5.35*10^- 3, ISO 68 is 4.81*10^-6 and SAE 85W-140 is 7.68*10^-6 and checking the damping ratio found that the value of damping factor neighboring to one(ξ=1) comparing to the others is SAE 90 oil type for this system. Analyzing the natural frequency of material which are Aluminum Alloy 1100 is 214.38hz,Copper Alloy is 440.70hz , Gray cast Iron is 684.00 hz Steel Alloy is 805.80 hz Carbon and low steel alloy (ASTM)is 491.94 from these material steel alloy is found to the best material for the transfer casing.